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Advanced PID type fuzzy logic power system stabilizer
Hiyama, Takashi; Kugimiya, Masahiko; Satoh, Hironori . Dept. of Electrical Engineering and Computer Science)
An advanced fuzzy logic control scheme has been proposed for a micro-computer based power system stabilizer to enhance the overall stability of power systems. The proposed control scheme utilizes the PID information of the generator speed. The input signal to the stabilizer is the real power output of a study unit. Simulations show the effectiveness of the advanced fuzzy logic control scheme.
NASA Technical Reports Server (NTRS)
Zadeh, Lofti A.
The author presents a condensed exposition of some basic ideas underlying fuzzy logic and describes some representative applications. The discussion covers basic principles; meaning representation and inference; basic rules of inference; and the linguistic variable and its application to fuzzy control.
Fuzzy logic controller optimization
Sepe, Jr., Raymond B; Miller, John Michael
A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.
Fuzzy logic in control systems: Fuzzy logic controller. I, II
NASA Technical Reports Server (NTRS)
Lee, Chuen Chien
Recent advances in the theory and applications of fuzzy-logic controllers (FLCs) are examined in an analytical review. The fundamental principles of fuzzy sets and fuzzy logic are recalled; the basic FLC components (fuzzification and defuzzification interfaces, knowledge base, and decision-making logic) are described; and the advantages of FLCs for incorporating expert knowledge into a control system are indicated. Particular attention is given to fuzzy implication functions, the interpretation of sentence connectives (and, also), compositional operators, and inference mechanisms. Applications discussed include the FLC-guided automobile developed by Sugeno and Nishida (1985), FLC hardware systems, FLCs for subway trains and ship-loading cranes, fuzzy-logic chips, and fuzzy computers.
NASA Technical Reports Server (NTRS)
Howard, Ayanna
The Fuzzy Logic Engine is a software package that enables users to embed fuzzy-logic modules into their application programs. Fuzzy logic is useful as a means of formulating human expert knowledge and translating it into software to solve problems. Fuzzy logic provides flexibility for modeling relationships between input and output information and is distinguished by its robustness with respect to noise and variations in system parameters. In addition, linguistic fuzzy sets and conditional statements allow systems to make decisions based on imprecise and incomplete information. The user of the Fuzzy Logic Engine need not be an expert in fuzzy logic: it suffices to have a basic understanding of how linguistic rules can be applied to the user's problem. The Fuzzy Logic Engine is divided into two modules: (1) a graphical-interface software tool for creating linguistic fuzzy sets and conditional statements and (2) a fuzzy-logic software library for embedding fuzzy processing capability into current application programs. The graphical- interface tool was developed using the Tcl/Tk programming language. The fuzzy-logic software library was written in the C programming language.
Fuzzy logic control of water level in advanced boiling water reactor
Lin, Chaung; Lee, Chi-Szu; Raghavan, R.; Fahrner, D.M.
The feedwater control system in the Advanced Boiling Water Reactor (ABWR) is more challenging to design compared to other control systems in the plant, due to the possible change in level from void collapses and swells during transient events. A basic fuzzy logic controller is developed using a simplified ABWR mathematical model to demonstrate and compare the performance of this controller with a simplified conventional controller. To reduce the design effort, methods are developed to automatically tune the scaling factors and control rules. As a first step in developing the fuzzy controller, a fuzzy controller with a limited number of rules is developed to respond to normal plant transients such as setpoint changes of plant parameters and load demand changes. Various simulations for setpoint and load demand changes of plant performances were conducted to evaluate the modeled fuzzy logic design against the simplified ABWR model control system. The simulation results show that the performance of the fuzzy logic controller is comparable to that of the Proportional-Integral (PI) controller, However, the fuzzy logic controller produced shorter settling time for step setpoint changes compared to the simplified conventional controller.
Zargham, M.R.
Recently, fuzzy logic has been applied to many areas, such as process control, image understanding, robots, expert systems, and decision support systems. This paper will explain the basic concepts of fuzzy logic and its application in different fields. The steps to design a control system will be explained in detail. Fuzzy control is the first successful industrial application of fuzzy logic. A fuzzy controller is able to control systems which previously could only be controlled by skilled operators. In recent years Japan has achieved significant progress in this area and has applied it to variety of products such as cruise control for cars, video cameras, rice cookers, washing machines, etc.
NASA Astrophysics Data System (ADS)
Malhas, Othman Qasim
The concept of "abacus logic" has recently been developed by the author (Malhas, n.d.). In this paper the relation of abacus logic to the concept of fuzziness is explored. It is shown that if a certain "regularity" condition is met, concepts from fuzzy set theory arise naturally within abacus logics. In particular it is shown that every abacus logic then has a "pre-Zadeh orthocomplementation". It is also shown that it is then possible to associate a fuzzy set with every proposition of abacus logic and that the collection of all such sets satisfies natural conditions expected in systems of fuzzy logic. Finally, the relevance to quantum mechanics is discussed.
Nonlinear Aerodynamic Modeling From Flight Data Using Advanced Piloted Maneuvers and Fuzzy Logic
NASA Technical Reports Server (NTRS)
Brandon, Jay M.; Morelli, Eugene A.
Results of the Aeronautics Research Mission Directorate Seedling Project Phase I research project entitled "Nonlinear Aerodynamics Modeling using Fuzzy Logic" are presented. Efficient and rapid flight test capabilities were developed for estimating highly nonlinear models of airplane aerodynamics over a large flight envelope. Results showed that the flight maneuvers developed, used in conjunction with the fuzzy-logic system identification algorithms, produced very good model fits of the data, with no model structure inputs required, for flight conditions ranging from cruise to departure and spin conditions.
Universal Approximation of Mamdani Fuzzy Controllers and Fuzzy Logical Controllers
NASA Technical Reports Server (NTRS)
Yuan, Bo; Klir, George J.
In this paper, we first distinguish two types of fuzzy controllers, Mamdani fuzzy controllers and fuzzy logical controllers. Mamdani fuzzy controllers are based on the idea of interpolation while fuzzy logical controllers are based on fuzzy logic in its narrow sense, i.e., fuzzy propositional logic. The two types of fuzzy controllers treat IF-THEN rules differently. In Mamdani fuzzy controllers, rules are treated disjunctively. In fuzzy logic controllers, rules are treated conjunctively. Finally, we provide a unified proof of the property of universal approximation for both types of fuzzy controllers.
Fuzzy logic mode switching in helicopters
NASA Technical Reports Server (NTRS)
Sherman, Porter D.; Warburton, Frank W.
The application of fuzzy logic to a wide range of control problems has been gaining momentum internationally, fueled by a concentrated Japanese effort. Advanced Research & Development within the Engineering Department at Sikorsky Aircraft undertook a fuzzy logic research effort designed to evaluate how effective fuzzy logic control might be in relation to helicopter operations. The mode switching module in the advanced flight control portion of Sikorsky's motion based simulator was identified as a good candidate problem because it was simple to understand and contained imprecise (fuzzy) decision criteria. The purpose of the switching module is to aid a helicopter pilot in entering and leaving coordinated turns while in flight. The criteria that determine the transitions between modes are imprecise and depend on the varied ranges of three flight conditions (i.e., simulated parameters): Commanded Rate, Duration, and Roll Attitude. The parameters were given fuzzy ranges and used as input variables to a fuzzy rulebase containing the knowledge of mode switching. The fuzzy control program was integrated into a real time interactive helicopter simulation tool. Optimization of the heading hold and turn coordination was accomplished by interactive pilot simulation testing of the handling quality performance of the helicopter dynamic model. The fuzzy logic code satisfied all the requirements of this candidate control problem.
Fuzzy logic particle tracking velocimetry
NASA Technical Reports Server (NTRS)
Wernet, Mark P.
Fuzzy logic has proven to be a simple and robust method for process control. Instead of requiring a complex model of the system, a user defined rule base is used to control the process. In this paper the principles of fuzzy logic control are applied to Particle Tracking Velocimetry (PTV). Two frames of digitally recorded, single exposure particle imagery are used as input. The fuzzy processor uses the local particle displacement information to determine the correct particle tracks. Fuzzy PTV is an improvement over traditional PTV techniques which typically require a sequence (greater than 2) of image frames for accurately tracking particles. The fuzzy processor executes in software on a PC without the use of specialized array or fuzzy logic processors. A pair of sample input images with roughly 300 particle images each, results in more than 200 velocity vectors in under 8 seconds of processing time.
NASA Technical Reports Server (NTRS)
A new all-electronic Particle Image Velocimetry technique that can efficiently map high speed gas flows has been developed in-house at the NASA Lewis Research Center. Particle Image Velocimetry is an optical technique for measuring the instantaneous two component velocity field across a planar region of a seeded flow field. A pulsed laser light sheet is used to illuminate the seed particles entrained in the flow field at two instances in time. One or more charged coupled device (CCD) cameras can be used to record the instantaneous positions of particles. Using the time between light sheet pulses and determining either the individual particle displacements or the average displacement of particles over a small subregion of the recorded image enables the calculation of the fluid velocity. Fuzzy logic minimizes the required operator intervention in identifying particles and computing velocity. Using two cameras that have the same view of the illumination plane yields two single exposure image frames. Two competing techniques that yield unambiguous velocity vector direction information have been widely used for reducing the single-exposure, multiple image frame data: (1) cross-correlation and (2) particle tracking. Correlation techniques yield averaged velocity estimates over subregions of the flow, whereas particle tracking techniques give individual particle velocity estimates. For the correlation technique, the correlation peak corresponding to the average displacement of particles across the subregion must be identified. Noise on the images and particle dropout result in misidentification of the true correlation peak. The subsequent velocity vector maps contain spurious vectors where the displacement peaks have been improperly identified. Typically these spurious vectors are replaced by a weighted average of the neighboring vectors, thereby decreasing the independence of the measurements. In this work, fuzzy logic techniques are used to determine the true
Knowledge representation in fuzzy logic
NASA Technical Reports Server (NTRS)
Zadeh, Lotfi A.
The author presents a summary of the basic concepts and techniques underlying the application of fuzzy logic to knowledge representation. He then describes a number of examples relating to its use as a computational system for dealing with uncertainty and imprecision in the context of knowledge, meaning, and inference. It is noted that one of the basic aims of fuzzy logic is to provide a computational framework for knowledge representation and inference in an environment of uncertainty and imprecision. In such environments, fuzzy logic is effective when the solutions need not be precise and/or it is acceptable for a conclusion to have a dispositional rather than categorical validity. The importance of fuzzy logic derives from the fact that there are many real-world applications which fit these conditions, especially in the realm of knowledge-based systems for decision-making and control.
Fuzzy logic and neural networks
Loos, J.R.
Combine fuzzy logic`s fuzzy sets, fuzzy operators, fuzzy inference, and fuzzy rules - like defuzzification - with neural networks and you can arrive at very unfuzzy real-time control. Fuzzy logic, cursed with a very whimsical title, simply means multivalued logic, which includes not only the conventional two-valued (true/false) crisp logic, but also the logic of three or more values. This means one can assign logic values of true, false, and somewhere in between. This is where fuzziness comes in. Multi-valued logic avoids the black-and-white, all-or-nothing assignment of true or false to an assertion. Instead, it permits the assignment of shades of gray. When assigning a value of true or false to an assertion, the numbers typically used are {open_quotes}1{close_quotes} or {open_quotes}0{close_quotes}. This is the case for programmed systems. If {open_quotes}0{close_quotes} means {open_quotes}false{close_quotes} and {open_quotes}1{close_quotes} means {open_quotes}true,{close_quotes} then {open_quotes}shades of gray{close_quotes} are any numbers between 0 and 1. Therefore, {open_quotes}nearly true{close_quotes} may be represented by 0.8 or 0.9, {open_quotes}nearly false{close_quotes} may be represented by 0.1 or 0.2, and {close_quotes}your guess is as good as mine{close_quotes} may be represented by 0.5. The flexibility available to one is limitless. One can associate any meaning, such as {open_quotes}nearly true{close_quotes}, to any value of any granularity, such as 0.9999. 2 figs.
Fuzzy logic of Aristotelian forms
Perlovsky, L.I.
Model-based approaches to pattern recognition and machine vision have been proposed to overcome the exorbitant training requirements of earlier computational paradigms. However, uncertainties in data were found to lead to a combinatorial explosion of the computational complexity. This issue is related here to the roles of a priori knowledge vs. adaptive learning. What is the a-priori knowledge representation that supports learning? I introduce Modeling Field Theory (MFT), a model-based neural network whose adaptive learning is based on a priori models. These models combine deterministic, fuzzy, and statistical aspects to account for a priori knowledge, its fuzzy nature, and data uncertainties. In the process of learning, a priori fuzzy concepts converge to crisp or probabilistic concepts. The MFT is a convergent dynamical system of only linear computational complexity. Fuzzy logic turns out to be essential for reducing the combinatorial complexity to linear one. I will discuss the relationship of the new computational paradigm to two theories due to Aristotle: theory of Forms and logic. While theory of Forms argued that the mind cannot be based on ready-made a priori concepts, Aristotelian logic operated with just such concepts. I discuss an interpretation of MFT suggesting that its fuzzy logic, combining a-priority and adaptivity, implements Aristotelian theory of Forms (theory of mind). Thus, 2300 years after Aristotle, a logic is developed suitable for his theory of mind.
Generalized interval-valued fuzzy variable precision rough sets determined by fuzzy logical operators
NASA Astrophysics Data System (ADS)
Qing Hu, Bao
The fuzzy rough set model and interval-valued fuzzy rough set model have been introduced to handle databases with real values and interval values, respectively. Variable precision rough set was advanced by Ziarko to overcome the shortcomings of misclassification and/or perturbation in Pawlak rough sets. By combining fuzzy rough set and variable precision rough set, a variety of fuzzy variable precision rough sets were studied, which cannot only handle numerical data, but are also less sensitive to misclassification. However, fuzzy variable precision rough sets cannot effectively handle interval-valued data-sets. Research into interval-valued fuzzy rough sets for interval-valued fuzzy data-sets has commenced; however, variable precision problems have not been considered in interval-valued fuzzy rough sets and generalized interval-valued fuzzy rough sets based on fuzzy logical operators nor have interval-valued fuzzy sets been considered in variable precision rough sets and fuzzy variable precision rough sets. These current models are incapable of wide application, especially on misclassification and/or perturbation and on interval-valued fuzzy data-sets. In this paper, these models are generalized to a more integrative approach that not only considers interval-valued fuzzy sets, but also variable precision. First, we review generalized interval-valued fuzzy rough sets based on two fuzzy logical operators: interval-valued fuzzy triangular norms and interval-valued fuzzy residual implicators. Second, we propose generalized interval-valued fuzzy variable precision rough sets based on the above two fuzzy logical operators. Finally, we confirm that some existing models, including rough sets, fuzzy variable precision rough sets, interval-valued fuzzy rough sets, generalized fuzzy rough sets and generalized interval-valued fuzzy variable precision rough sets based on fuzzy logical operators, are special cases of the proposed models.
Fuzzy Logic in Medicine and Bioinformatics
Torres, Angela; Nieto, Juan J.
The purpose of this paper is to present a general view of the current applications of fuzzy logic in medicine and bioinformatics. We particularly review the medical literature using fuzzy logic. We then recall the geometrical interpretation of fuzzy sets as points in a fuzzy hypercube and present two concrete illustrations in medicine (drug addictions) and in bioinformatics (comparison of genomes). PMID:16883057
NASA Technical Reports Server (NTRS)
Ruspini, Enrique H.
Summarized here are the results of recent research on the conceptual foundations of fuzzy logic. The focus is primarily on the principle characteristics of a model that quantifies resemblance between possible worlds by means of a similarity function that assigns a number between 0 and 1 to every pair of possible worlds. Introduction of such a function permits one to interpret the major constructs and methods of fuzzy logic: conditional and unconditional possibility and necessity distributions and the generalized modus ponens of Zadeh on the basis of related metric relationships between subsets of possible worlds.
Designing a Software Tool for Fuzzy Logic Programming
NASA Astrophysics Data System (ADS)
Abietar, José M.; Morcillo, Pedro J.; Moreno, Ginés
Fuzzy Logic Programming is an interesting and still growing research area that agglutinates the efforts for introducing fuzzy logic into logic programming (LP), in order to incorporate more expressive resources on such languages for dealing with uncertainty and approximated reasoning. The multi-adjoint logic programming approach is a recent and extremely flexible fuzzy logic paradigm for which, unfortunately, we have not found practical tools implemented so far. In this work, we describe a prototype system which is able to directly translate fuzzy logic programs into Prolog code in order to safely execute these residual programs inside any standard Prolog interpreter in a completely transparent way for the final user. We think that the development of such fuzzy languages and programing tools might play an important role in the design of advanced software applications for computational physics, chemistry, mathematics, medicine, industrial control and so on.
Soft computing and fuzzy logic
Zadeh, L.A.
Soft computing is a collection of methodologies that aim to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low solution cost. Its principal constituents are fuzzy logic, neuro-computing, and probabilistic reasoning. Soft computing is likely to play an increasingly important role in many application areas, including software engineering. The role model for soft computing is the human mind.
Learning fuzzy logic control system
NASA Technical Reports Server (NTRS)
Lung, Leung Kam
The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the
The cognitive bases for the design of a new class of fuzzy logic controllers: The clearness transformation fuzzy logic controller
NASA Technical Reports Server (NTRS)
Sultan, Labib; Janabi, Talib
This paper analyses the internal operation of fuzzy logic controllers as referenced to the human cognitive tasks of control and decision making. Two goals are targeted. The first goal focuses on the cognitive interpretation of the mechanisms employed in the current design of fuzzy logic controllers. This analysis helps to create a ground to explore the potential of enhancing the functional intelligence of fuzzy controllers. The second goal is to outline the features of a new class of fuzzy controllers, the Clearness Transformation Fuzzy Logic Controller (CT-FLC), whereby some new concepts are advanced to qualify fuzzy controllers as 'cognitive devices' rather than 'expert system devices'. The operation of the CT-FLC, as a fuzzy pattern processing controller, is explored, simulated, and evaluated.
Fuzzy logic control of telerobot manipulators
NASA Technical Reports Server (NTRS)
Franke, Ernest A.; Nedungadi, Ashok
Telerobot systems for advanced applications will require manipulators with redundant 'degrees of freedom' (DOF) that are capable of adapting manipulator configurations to avoid obstacles while achieving the user specified goal. Conventional methods for control of manipulators (based on solution of the inverse kinematics) cannot be easily extended to these situations. Fuzzy logic control offers a possible solution to these needs. A current research program at SRI developed a fuzzy logic controller for a redundant, 4 DOF, planar manipulator. The manipulator end point trajectory can be specified by either a computer program (robot mode) or by manual input (teleoperator). The approach used expresses end-point error and the location of manipulator joints as fuzzy variables. Joint motions are determined by a fuzzy rule set without requiring solution of the inverse kinematics. Additional rules for sensor data, obstacle avoidance and preferred manipulator configuration, e.g., 'righty' or 'lefty', are easily accommodated. The procedure used to generate the fuzzy rules can be extended to higher DOF systems.
Fuzzy logic based robotic controller
NASA Technical Reports Server (NTRS)
Attia, F.; Upadhyaya, M.
Existing Proportional-Integral-Derivative (PID) robotic controllers rely on an inverse kinematic model to convert user-specified cartesian trajectory coordinates to joint variables. These joints experience friction, stiction, and gear backlash effects. Due to lack of proper linearization of these effects, modern control theory based on state space methods cannot provide adequate control for robotic systems. In the presence of loads, the dynamic behavior of robotic systems is complex and nonlinear, especially where mathematical modeling is evaluated for real-time operators. Fuzzy Logic Control is a fast emerging alternative to conventional control systems in situations where it may not be feasible to formulate an analytical model of the complex system. Fuzzy logic techniques track a user-defined trajectory without having the host computer to explicitly solve the nonlinear inverse kinematic equations. The goal is to provide a rule-based approach, which is closer to human reasoning. The approach used expresses end-point error, location of manipulator joints, and proximity to obstacles as fuzzy variables. The resulting decisions are based upon linguistic and non-numerical information. This paper presents a solution to the conventional robot controller which is independent of computationally intensive kinematic equations. Computer simulation results of this approach as obtained from software implementation are also discussed.
Fuzzy Versions of Epistemic and Deontic Logic
NASA Technical Reports Server (NTRS)
Gounder, Ramasamy S.; Esterline, Albert C.
Epistemic and deontic logics are modal logics, respectively, of knowledge and of the normative concepts of obligation, permission, and prohibition. Epistemic logic is useful in formalizing systems of communicating processes and knowledge and belief in AI (Artificial Intelligence). Deontic logic is useful in computer science wherever we must distinguish between actual and ideal behavior, as in fault tolerance and database integrity constraints. We here discuss fuzzy versions of these logics. In the crisp versions, various axioms correspond to various properties of the structures used in defining the semantics of the logics. Thus, any axiomatic theory will be characterized not only by its axioms but also by the set of properties holding of the corresponding semantic structures. Fuzzy logic does not proceed with axiomatic systems, but fuzzy versions of the semantic properties exist and can be shown to correspond to some of the axioms for the crisp systems in special ways that support dependency networks among assertions in a modal domain. This in turn allows one to implement truth maintenance systems. For the technical development of epistemic logic, and for that of deontic logic. To our knowledge, we are the first to address fuzzy epistemic and fuzzy deontic logic explicitly and to consider the different systems and semantic properties available. We give the syntax and semantics of epistemic logic and discuss the correspondence between axioms of epistemic logic and properties of semantic structures. The same topics are covered for deontic logic. Fuzzy epistemic and fuzzy deontic logic discusses the relationship between axioms and semantic properties for these logics. Our results can be exploited in truth maintenance systems.
Fuzzy logic components for iterative deconvolution systems
NASA Astrophysics Data System (ADS)
Northan, Brian M.
Deconvolution systems rely heavily on expert knowledge and would benefit from approaches that capture this expert knowledge. Fuzzy logic is an approach that is used to capture expert knowledge rules and produce outputs that range in degree. This paper describes a fuzzy-deconvolution-system that integrates traditional Richardson-Lucy deconvolution with fuzzy components. The system is intended for restoration of 3D widefield images taken under conditions of refractive index mismatch. The system uses a fuzzy rule set for calculating sample refractive index, a fuzzy median filter for inter-iteration noise reduction, and a fuzzy rule set for stopping criteria.
Fuzzy logic and coarse coding using programmable logic devices
NASA Astrophysics Data System (ADS)
Brooks, Geoffrey
Naturally-occurring sensory signal processing algorithms, such as those that inspired fuzzy-logic control, can be integrated into non-naturally-occurring high-performance technology, such as programmable logic devices, to realize novel bio-inspired designs. Research is underway concerning an investigation into using field programmable logic devices (FPLD's) to implement fuzzy logic sensory processing. A discussion is provided concerning the commonality between bio-inspired fuzzy logic algorithms and coarse coding that is prevalent in naturally-occurring sensory systems. Undergraduate design projects using fuzzy logic for an obstacle-avoidance robot has been accomplished at our institution and other places; numerous other successful fuzzy logic applications can be found as well. The long-term goal is to leverage such biomimetic algorithms for future applications. This paper outlines a design approach for implementing fuzzy-logic algorithms into reconfigurable computing devices. This paper is presented in an effort to connect with others who may be interested in collaboration as well as to establish a starting point for future research.
Refining fuzzy logic controllers with machine learning
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.
Development of Fuzzy Logic and Neural Network Control and Advanced Emissions Modeling for Parallel Hybrid Vehicles
Rajagopalan, A.; Washington, G.; Rizzoni, G.; Guezennec, Y.
This report describes the development of new control strategies and models for Hybrid Electric Vehicles (HEV) by the Ohio State University. The report indicates results from models created in NREL's ADvanced VehIcle SimulatOR (ADVISOR 3.2), and results of a scalable IC Engine model, called in Willan's Line technique, implemented in ADVISOR 3.2.
Application of Fuzzy Logic to Matrix FMECA
NASA Astrophysics Data System (ADS)
Shankar, N. Ravi; Prabhu, B. S.
A methodology combining the benefits of Fuzzy Logic and Matrix FMEA is presented in this paper. The presented methodology extends the risk prioritization beyond the conventional Risk Priority Number (RPN) method. Fuzzy logic is used to calculate the criticality rank. Also the matrix approach is improved further to develop a pictorial representation retaining all relevant qualitative and quantitative information of several FMEA elements relationships. The methodology presented is demonstrated by application to an illustrative example.
Fuzzy logic and neural network technologies
NASA Technical Reports Server (NTRS)
Villarreal, James A.; Lea, Robert N.; Savely, Robert T.
Applications of fuzzy logic technologies in NASA projects are reviewed to examine their advantages in the development of neural networks for aerospace and commercial expert systems and control. Examples of fuzzy-logic applications include a 6-DOF spacecraft controller, collision-avoidance systems, and reinforcement-learning techniques. The commercial applications examined include a fuzzy autofocusing system, an air conditioning system, and an automobile transmission application. The practical use of fuzzy logic is set in the theoretical context of artificial neural systems (ANSs) to give the background for an overview of ANS research programs at NASA. The research and application programs include the Network Execution and Training Simulator and faster training algorithms such as the Difference Optimized Training Scheme. The networks are well suited for pattern-recognition applications such as predicting sunspots, controlling posture maintenance, and conducting adaptive diagnoses.
Fuzzy logic control for camera tracking system
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Fritz, R. H.; Giarratano, J.; Jani, Yashvant
A concept utilizing fuzzy theory has been developed for a camera tracking system to provide support for proximity operations and traffic management around the Space Station Freedom. Fuzzy sets and fuzzy logic based reasoning are used in a control system which utilizes images from a camera and generates required pan and tilt commands to track and maintain a moving target in the camera's field of view. This control system can be implemented on a fuzzy chip to provide an intelligent sensor for autonomous operations. Capabilities of the control system can be expanded to include approach, handover to other sensors, caution and warning messages.
Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance
Park, Jong-Wook; Kwak, Hwan-Joo; Kang, Young-Chang; Kim, Dong W.
An advanced fuzzy potential field method for mobile robot obstacle avoidance is proposed. The potential field method primarily deals with the repulsive forces surrounding obstacles, while fuzzy control logic focuses on fuzzy rules that handle linguistic variables and describe the knowledge of experts. The design of a fuzzy controller—advanced fuzzy potential field method (AFPFM)—that models and enhances the conventional potential field method is proposed and discussed. This study also examines the rule-explosion problem of conventional fuzzy logic and assesses the performance of our proposed AFPFM through simulations carried out using a mobile robot. PMID:27123001
Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance.
Park, Jong-Wook; Kwak, Hwan-Joo; Kang, Young-Chang; Kim, Dong W
An advanced fuzzy potential field method for mobile robot obstacle avoidance is proposed. The potential field method primarily deals with the repulsive forces surrounding obstacles, while fuzzy control logic focuses on fuzzy rules that handle linguistic variables and describe the knowledge of experts. The design of a fuzzy controller--advanced fuzzy potential field method (AFPFM)--that models and enhances the conventional potential field method is proposed and discussed. This study also examines the rule-explosion problem of conventional fuzzy logic and assesses the performance of our proposed AFPFM through simulations carried out using a mobile robot. PMID:27123001
Fuzzy logic controllers: From development to deployment
Bonissone, P.P.; Chiang, K.H.
We view fuzzy logic control technology as a high level language in which we can efficiently define and synthesize non-linear controllers for a given process. We contrast fuzzy Proportional Integral (PI) controllers with conventional PI and two dimensional sliding mode controllers. Then we compare the development of Fuzzy Logic Controllers (FLC) with that of Knowledge Based System (KBS) applications. We decompose the comparison into reasoning tasks (representation, inference, and control) and application tasks (acquisition, development, validation, compilation, and deployment). After reviewing the reasoning tasks, we focus on the compilation of fuzzy rule bases into fast access lookup tables. These tables can be used by a simplified run-time engine to determine the TLC`s crisp output for a given input.
Fuzzy logic in autonomous orbital operations
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Jani, Yashvant
Fuzzy logic can be used advantageously in autonomous orbital operations that require the capability of handling imprecise measurements from sensors. Several applications are underway to investigate fuzzy logic approaches and develop guidance and control algorithms for autonomous orbital operations. Translational as well as rotational control of a spacecraft have been demonstrated using space shuttle simulations. An approach to a camera tracking system has been developed to support proximity operations and traffic management around the Space Station Freedom. Pattern recognition and object identification algorithms currently under development will become part of this camera system at an appropriate level in the future. A concept to control environment and life support systems for large Lunar based crew quarters is also under development. Investigations in the area of reinforcement learning, utilizing neural networks, combined with a fuzzy logic controller, are planned as a joint project with the Ames Research Center.
Pattern recognition using linguistic fuzzy logic predictors
NASA Astrophysics Data System (ADS)
Habiballa, Hashim
The problem of pattern recognition has been solved with numerous methods in the Artificial Intelligence field. We present an unconventional method based on Lingustic Fuzzy Logic Forecaster which is primarily used for the task of time series analysis and prediction through logical deduction wtih linguistic variables. This method should be used not only to the time series prediction itself, but also for recognition of patterns in a signal with seasonal component.
Automated cloud classification with a fuzzy logic expert system
NASA Technical Reports Server (NTRS)
Tovinkere, Vasanth; Baum, Bryan A.
An unresolved problem in current cloud retrieval algorithms concerns the analysis of scenes containing overlapping cloud layers. Cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget. Most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. One promising method uses fuzzy logic to determine whether mixed cloud and/or surface types exist within a group of pixels, such as cirrus, land, and water, or cirrus and stratus. When two or more class types are present, fuzzy logic uses membership values to assign the group of pixels partially to the different class types. The strength of fuzzy logic lies in its ability to work with patterns that may include more than one class, facilitating greater information extraction from satellite radiometric data. The development of the fuzzy logic rule-based expert system involves training the fuzzy classifier with spectral and textural features calculated from accurately labeled 32x32 regions of Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data. The spectral data consists of AVHRR channels 1 (0.55-0.68 mu m), 2 (0.725-1.1 mu m), 3 (3.55-3.93 mu m), 4 (10.5-11.5 mu m), and 5 (11.5-12.5 mu m), which include visible, near-infrared, and infrared window regions. The textural features are based on the gray level difference vector (GLDV) method. A sophisticated new interactive visual image Classification System (IVICS) is used to label samples chosen from scenes collected during the FIRE IFO II. The training samples are chosen from predefined classes, chosen to be ocean, land, unbroken stratiform, broken stratiform, and cirrus. The November 28, 1991 NOAA overpasses contain complex multilevel cloud situations ideal for training and validating the fuzzy logic expert system.
The paper discusses the fuzzy logic control (FLC) of electric motors, being investigated under the sponsorship of the U.S. EPA to reduce energy consumption when motors are operated at less than rated speeds and loads. lectric motors use 60% of the electrical energy generated in t...
Indeterminacy, linguistic semantics and fuzzy logic
Novak, V.
In this paper, we discuss the indeterminacy phenomenon which has two distinguished faces, namely uncertainty modeled especially by the probability theory and vagueness, modeled by fuzzy logic. Other important mathematical model of vagueness is provided by the Alternative Set Theory. We focus on some of the basic concepts of these theories in connection with mathematical modeling of the linguistic semantics.
Neurocontrol and fuzzy logic: Connections and designs
NASA Technical Reports Server (NTRS)
Werbos, Paul J.
Artificial neural networks (ANNs) and fuzzy logic are complementary technologies. ANNs extract information from systems to be learned or controlled, while fuzzy techniques mainly use verbal information from experts. Ideally, both sources of information should be combined. For example, one can learn rules in a hybrid fashion, and then calibrate them for better whole-system performance. ANNs offer universal approximation theorems, pedagogical advantages, very high-throughput hardware, and links to neurophysiology. Neurocontrol - the use of ANNs to directly control motors or actuators, etc. - uses five generalized designs, related to control theory, which can work on fuzzy logic systems as well as ANNs. These designs can copy what experts do instead of what they say, learn to track trajectories, generalize adaptive control, and maximize performance or minimize cost over time, even in noisy environments. Design tradeoffs and future directions are discussed throughout.
The Impact of Fuzzy Logic on Student Press Law.
ERIC Educational Resources Information Center
McCool, Lauralee; Plopper, Bruce L.
Uses the relatively new science of fuzzy logic to review lower court and appellate court decisions from the last four decades regarding free expression in student publications. Finds pronounced effects, showing that fuzzy sets inherently favor administrators, while students show a strikingly high win/loss ratio when courts avoid fuzzy logic. (SR)
Astronomical pipeline processing using fuzzy logic
NASA Astrophysics Data System (ADS)
Shamir, Lior; Nemiroff, Robert J. Nemiroff
Fundamental astronomical questions on the composition of the universe, the abundance of Earth-like planets, and the cause of the brightest explosions in the universe are being attacked by robotic telescopes costing billions of dollars and returning vast pipelines of data. The success of these programs depends on the accuracy of automated real time processing of images never seen by a human, and all predicated on fast and accurate automatic identifications of known astronomical objects and new astronomical transients. In this paper the needs of modern astronomical pipelines are discussed in the light of fuzzy-logic based decision-making. Several specific fuzzy-logic algorithms have been develop for the first time for astronomical purposes, and tested with excellent results on a test pipeline of data from the existing Night Sky Live sky survey.
Fuzzy logic controller to improve powerline communication
NASA Astrophysics Data System (ADS)
Tirrito, Salvatore
The Power Line Communications (PLC) technology allows the use of the power grid in order to ensure the exchange of data information among devices. This work proposes an approach, based on Fuzzy Logic, that dynamically manages the amplitude of the signal, with which each node transmits, by processing the master-slave link quality measured and the master-slave distance. The main objective of this is to reduce both the impact of communication interferences induced and power consumption.
NASA Astrophysics Data System (ADS)
Kelkar, Nikhal; Samu, Tayib; Hall, Ernest L.
Automated guided vehicles (AGVs) have many potential applications in manufacturing, medicine, space and defense. The purpose of this paper is to describe exploratory research on the design of a modular autonomous mobile robot controller. The controller incorporates a fuzzy logic approach for steering and speed control, a neuro-fuzzy approach for ultrasound sensing (not discussed in this paper) and an overall expert system. The advantages of a modular system are related to portability and transportability, i.e. any vehicle can become autonomous with minimal modifications. A mobile robot test-bed has been constructed using a golf cart base. This cart has full speed control with guidance provided by a vision system and obstacle avoidance using ultrasonic sensors. The speed and steering fuzzy logic controller is supervised by a 486 computer through a multi-axis motion controller. The obstacle avoidance system is based on a micro-controller interfaced with six ultrasonic transducers. This micro- controller independently handles all timing and distance calculations and sends a steering angle correction back to the computer via the serial line. This design yields a portable independent system in which high speed computer communication is not necessary. Vision guidance is accomplished with a CCD camera with a zoom lens. The data is collected by a vision tracking device that transmits the X, Y coordinates of the lane marker to the control computer. Simulation and testing of these systems yielded promising results. This design, in its modularity, creates a portable autonomous fuzzy logic controller applicable to any mobile vehicle with only minor adaptations.
Fuzzy Logic Connectivity in Semiconductor Defect Clustering
Gleason, S.S.; Kamowski, T.P.; Tobin, K.W.
In joining defects on semiconductor wafer maps into clusters, it is common for defects caused by different sources to overlap. Simple morphological image processing tends to either join too many unrelated defects together or not enough together. Expert semiconductor fabrication engineers have demonstrated that they can easily group clusters of defects from a common manufacturing problem source into a single signature. Capturing this thought process is ideally suited for fuzzy logic. A system of rules was developed to join disconnected clusters based on properties such as elongation, orientation, and distance. The clusters are evaluated on a pair-wise basis using the fuzzy rules and are joined or not joined based on a defuzzification and threshold. The system continuously re-evaluates the clusters under consideration as their fuzzy memberships change with each joining action. The fuzzy membership functions for each pair-wise feature, the techniques used to measure the features, and methods for improving the speed of the system are all developed. Examples of the process are shown using real-world semiconductor wafer maps obtained from chip manufacturers. The algorithm is utilized in the Spatial Signature Analyzer (SSA) software, a joint development project between Oak Ridge National Lab (ORNL) and SEMATECH.
Improving Cooperative PSO using Fuzzy Logic
NASA Astrophysics Data System (ADS)
Afsahi, Zahra; Meybodi, Mohammadreza
PSO is a population-based technique for optimization, which simulates the social behaviour of the fish schooling or bird flocking. Two significant weaknesses of this method are: first, falling into local optimum and second, the curse of dimensionality. In this work we present the FCPSO-H to overcome these weaknesses. Our approach was implemented in the cooperative PSO, which employs fuzzy logic to control the acceleration coefficients in velocity equation of each particle. The proposed approach is validated by function optimization problem form the standard literature simulation result indicates that the approach is highly competitive specifically in its better general convergence performance.
Fuzzy logic and guidance algorithm design
Leng, G.
This paper explores the use of fuzzy logic for the design of a terminal guidance algorithm for an air to surface missile against a stationary target. The design objectives are (1) a smooth transition, at lock-on, (2) large impact angles and (3) self-limiting acceleration commands. The method of reverse kinematics is used in the design of the membership functions and the rule base. Simulation results for a Mach 0.8 missile with a 6g acceleration limit are compared with a traditional proportional navigation scheme.
Robust fuzzy logic control of mechanical systems
NASA Astrophysics Data System (ADS)
Kohn-Rich, Sylvia
An approach for the design of robust fuzzy control laws for a large class of mechanical systems was developed. The approach applies Lyapunov's Stability Theory to ensure closed loop stability in the presence of plant perturbations and bounded disturbances. It uses inherent properties of an important class of mechanical and aerospace systems, such as robotic manipulators and large spacecraft, to derive closed-loop stability conditions. Based on these conditions, a methodology for the design of robust fuzzy control systems with guaranteed closed-loop stability was developed. Two classes of control laws for mechanical systems were considered. First, a methodology for point-to-point control was formulated. It combines an energy-type approach with Lyapunov's Stability Theory and its extensions, to obtain robust stability conditions for the closed-loop system. A procedure for control system development based on the above conditions is presented. Finally, a procedure for the implementation of the fuzzy control system with guaranteed performance and closed-loop stability characteristics is formulated. In the second part of the dissertation, the problem of robust tracking for mechanical systems was considered. Based on Lyapunov's Stability Theory and its extensions due to Leitmann and Corless, conditions were developed to prove robust stability and performance in the presence of plant uncertainties, bounded disturbances and control saturation. These conditions involve a large number of parameters and functional dependencies that can be chosen by the designer, therefore are well suited for Fuzzy Logic Control implementation. Three different fuzzy implementation methods for the proposed controls system were analyzed and their relative advantages were discussed. An extensive simulation study of the proposed approach was conducted. It demonstrated the excellent performance of the proposed control systems. The proposed method showed superior performance compared to other robust
The Influence of Fuzzy Logic Theory on Students' Achievement
ERIC Educational Resources Information Center
Semerci, Çetin
As science and technology develop, the use's areas of Fuzzy Logic Theory develop too. Measurement and evaluation in education is one of these areas. The purpose of this research is to explain the influence of fuzzy logic theory on students' achievement. An experimental method is employed in the research. The traditional achievement marks and The…
Terminology and concepts of control and Fuzzy Logic
NASA Technical Reports Server (NTRS)
Aldridge, Jack; Lea, Robert; Jani, Yashvant; Weiss, Jonathan
Viewgraphs on terminology and concepts of control and fuzzy logic are presented. Topics covered include: control systems; issues in the design of a control system; state space control for inverted pendulum; proportional-integral-derivative (PID) controller; fuzzy controller; and fuzzy rule processing.
Active structural control by fuzzy logic rules: An introduction
Tang, Yu; Wu, Kung C.
A zeroth level introduction to fuzzy logic control applied to the active structural control to reduce the dynamic response of structures subjected to earthquake excitations is presented. It is hoped that this presentation will increase the attractiveness of the methodology to structural engineers in research as well as in practice. The basic concept of the fuzzy logic control are explained by examples and by diagrams with a minimum of mathematics. The effectiveness and simplicity of the fuzzy logic control is demonstrated by a numerical example in which the response of a single- degree-of-freedom system subjected to earthquake excitations is controlled by making use of the fuzzy logic controller. In the example, the fuzzy rules are first learned from the results obtained from linear control theory; then they are fine tuned to improve their performance. It is shown that the performance of fuzzy logic control surpasses that of the linear control theory. The paper shows that linear control theory provides experience for fuzzy logic control, and fuzzy logic control can provide better performance; therefore, two controllers complement each other.
Active structural control by fuzzy logic rules: An introduction
Tang, Y.
An introduction to fuzzy logic control applied to the active structural control to reduce the dynamic response of structures subjected to earthquake excitations is presented. It is hoped that this presentation will increase the attractiveness of the methodology to structural engineers in research as well as in practice. The basic concept of the fuzzy logic control are explained by examples and by diagrams with a minimum of mathematics. The effectiveness and simplicity of the fuzzy logic control is demonstrated by a numerical example in which the response of a single-degree-of-freedom system subjected to earthquake excitations is controlled by making use of the fuzzy logic controller. In the example, the fuzzy rules are first learned from the results obtained from linear control theory; then they are fine tuned to improve their performance. It is shown that the performance of fuzzy logic control surpasses that of the linear control theory. The paper shows that linear control theory provides experience for fuzzy logic control, and fuzzy logic control can provide better performance; therefore, two controllers complement each other.
Control of a fluidized bed combustor using fuzzy logic
Koffman, S.J.; Brown, R.C.; Fullmer, R.R.
Fuzzy logic--an artificial intelligence technique--can be employed to exploit the wealth of information human experts have learned about complex systems while attempting to control them. This information is usually of a qualitative nature that is unusable by rigid conventional control techniques. Fuzzy logic, uses as a control method, manipulates linguistically expressed, heuristic knowledge from a human expert to derive control actions for a described system. As an alternative approach to classical controls, fuzzy logic is examined for start-up control and normal regulation of a bubbling fluidized bed combustor. To validate the fuzzy logic approach, the fuzzy controller is compared to a classical proportional and integral (PI) controller, commonly used in industrial applications, designed by Ziegler-Nichols tuning.
Fuzzy Logic and Its Application in Football Team Ranking
Li, Junhong
Fuzzy set theory and fuzzy logic are a highly suitable and applicable basis for developing knowledge-based systems in physical education for tasks such as the selection for athletes, the evaluation for different training approaches, the team ranking, and the real-time monitoring of sports data. In this paper, we use fuzzy set theory and apply fuzzy clustering analysis in football team ranking. Based on some certain rules, we propose four parameters to calculate fuzzy similar matrix, obtain fuzzy equivalence matrix and the ranking result for our numerical example, T7, T3, T1, T9, T10, T8, T11, T12, T2, T6, T5, T4, and investigate four parameters sensitivity analysis. The study shows that our fuzzy logic method is reliable and stable when the parameters change in certain range. PMID:25032227
A simple fuzzy logic real-time camera tracking system
NASA Technical Reports Server (NTRS)
Magee, Kevin N.; Cheatham, John B., Jr.
A fuzzy logic control of camera pan and tilt has been implemented to provide real-time camera tracking of a moving object. The user clicks a mouse button to identify the object that is to be tracked. A rapid centroid estimation algorithm is used to estimate the location of the moving object, and based on simple fuzzy membership functions, fuzzy x and y values are input into a six-rule fuzzy logic rule base. The output of this system is de-fuzzified to provide pan and tilt velocities required to keep the image of the object approximately centered in the camera field of view.
Fruit Sorting Using Fuzzy Logic Techniques
NASA Astrophysics Data System (ADS)
Elamvazuthi, Irraivan; Sinnadurai, Rajendran; Aftab Ahmed Khan, Mohamed Khan; Vasant, Pandian
Fruit and vegetables market is getting highly selective, requiring their suppliers to distribute the goods according to very strict standards of quality and presentation. In the last years, a number of fruit sorting and grading systems have appeared to fulfill the needs of the fruit processing industry. However, most of them are overly complex and too costly for the small and medium scale industry (SMIs) in Malaysia. In order to address these shortcomings, a prototype machine was developed by integrating the fruit sorting, labeling and packing processes. To realise the prototype, many design issues were dealt with. Special attention is paid to the electronic weighing sub-system for measuring weight, and the opto-electronic sub-system for determining the height and width of the fruits. Specifically, this paper discusses the application of fuzzy logic techniques in the sorting process.
Fuzzy Logic Enhanced Digital PIV Processing Software
NASA Technical Reports Server (NTRS)
Wernet, Mark P.
Digital Particle Image Velocimetry (DPIV) is an instantaneous, planar velocity measurement technique that is ideally suited for studying transient flow phenomena in high speed turbomachinery. DPIV is being actively used at the NASA Glenn Research Center to study both stable and unstable operating conditions in a high speed centrifugal compressor. Commercial PIV systems are readily available which provide near real time feedback of the PIV image data quality. These commercial systems are well designed to facilitate the expedient acquisition of PIV image data. However, as with any general purpose system, these commercial PIV systems do not meet all of the data processing needs required for PIV image data reduction in our compressor research program. An in-house PIV PROCessing (PIVPROC) code has been developed for reducing PIV data. The PIVPROC software incorporates fuzzy logic data validation for maximum information recovery from PIV image data. PIVPROC enables combined cross-correlation/particle tracking wherein the highest possible spatial resolution velocity measurements are obtained.
Dynamic regimes of random fuzzy logic networks
NASA Astrophysics Data System (ADS)
Wittmann, Dominik M.; Theis, Fabian J.
Random multistate networks, generalizations of the Boolean Kauffman networks, are generic models for complex systems of interacting agents. Depending on their mean connectivity, these networks exhibit ordered as well as chaotic behavior with a critical boundary separating both regimes. Typically, the nodes of these networks are assigned single discrete states. Here, we describe nodes by fuzzy numbers, i.e. vectors of degree-of-membership (DOM) functions specifying the degree to which the nodes are in each of their discrete states. This allows our models to deal with imprecision and uncertainties. Compatible update rules are constructed by expressing the update rules of the multistate network in terms of Boolean operators and generalizing them to fuzzy logic (FL) operators. The standard choice for these generalizations is the Gödel FL, where AND and OR are replaced by the minimum and maximum of two DOMs, respectively. In mean-field approximations we are able to analytically describe the percolation and asymptotic distribution of DOMs in random Gödel FL networks. This allows us to characterize the different dynamic regimes of random multistate networks in terms of FL. In a low-dimensional example, we provide explicit computations and validate our mean-field results by showing that they agree well with network simulations.
Twenty-Five Years of the Fuzzy Factor: Fuzzy Logic, the Courts, and Student Press Law.
ERIC Educational Resources Information Center
Plopper, Bruce L.; McCool, Lauralee
A study applied the structure of fuzzy logic, a fairly modern development in mathematical set theory, to judicial opinions concerning non-university, public school student publications, from 1975 to 1999. The study examined case outcomes (19 cases generated 27 opinions) as a function of fuzzy logic, and it evaluated interactions between fuzzy…
A fuzzy logic approach to modeling a vehicle crash test
NASA Astrophysics Data System (ADS)
Pawlus, Witold; Karimi, Hamid; Robbersmyr, Kjell
This paper presents an application of fuzzy approach to vehicle crash modeling. A typical vehicle to pole collision is described and kinematics of a car involved in this type of crash event is thoroughly characterized. The basics of fuzzy set theory and modeling principles based on fuzzy logic approach are presented. In particular, exceptional attention is paid to explain the methodology of creation of a fuzzy model of a vehicle collision. Furthermore, the simulation results are presented and compared to the original vehicle's kinematics. It is concluded which factors have influence on the accuracy of the fuzzy model's output and how they can be adjusted to improve the model's fidelity.
An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller
ERIC Educational Resources Information Center
Mamdani, E. H.; Assilian, S.
This paper describes an experiment on the "linguistic" synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. (Author)
Organizational coevolutionary classifiers with fuzzy logic used in intrusion detection
NASA Astrophysics Data System (ADS)
Chen, Zhenguo
Intrusion detection is an important technique in the defense-in-depth network security framework and a hot topic in computer security in recent years. To solve the intrusion detection question, we introduce the fuzzy logic into Organization CoEvolutionary algorithm [1] and present the algorithm of Organization CoEvolutionary Classification with Fuzzy Logic. In this paper, we give an intrusion detection models based on Organization CoEvolutionary Classification with Fuzzy Logic. After illustrating our model with a representative dataset and applying it to the real-world network datasets KDD Cup 1999. The experimental result shown that the intrusion detection based on Organizational Coevolutionary Classifiers with Fuzzy Logic can give higher recognition accuracy than the general method.
Fuzzy logic controllers: A knowledge-based system perspective
NASA Technical Reports Server (NTRS)
Bonissone, Piero P.
Over the last few years we have seen an increasing number of applications of Fuzzy Logic Controllers. These applications range from the development of auto-focus cameras, to the control of subway trains, cranes, automobile subsystems (automatic transmissions), domestic appliances, and various consumer electronic products. In summary, we consider a Fuzzy Logic Controller to be a high level language with its local semantics, interpreter, and compiler, which enables us to quickly synthesize non-linear controllers for dynamic systems.
Fuzzy logic based ELF magnetic field estimation in substations.
Kosalay, Ilhan
This paper examines estimation of the extremely low frequency magnetic fields (MF) in the power substation. First, the results of the previous relevant research studies and the MF measurements in a sample power substation are presented. Then, a fuzzy logic model based on the geometric definitions in order to estimate the MF distribution is explained. Visual software, which has a three-dimensional screening unit, based on the fuzzy logic technique, has been developed. PMID:18440967
A Priority Fuzzy Logic Extension of the XQuery Language
NASA Astrophysics Data System (ADS)
Škrbić, Srdjan; Wettayaprasit, Wiphada; Saeueng, Pannipa
In recent years there have been significant research findings in flexible XML querying techniques using fuzzy set theory. Many types of fuzzy extensions to XML data model and XML query languages have been proposed. In this paper, we introduce priority fuzzy logic extensions to XQuery language. Describing these extensions we introduce a new query language. Moreover, we describe a way to implement an interpreter for this language using an existing XML native database.
A new way of predicting cement strength -- Fuzzy logic
Gao Faliang
This paper is to analyze the fuzzy logic method of predicting cement strength and to calculate some samples with fuzzy models. In order to compare, samples of them are calculated with regression method. All of results are shown in both root mean square error and scattered map.
Optimized parameter extraction using fuzzy logic
NASA Astrophysics Data System (ADS)
Picos, Rodrigo; Calvo, Oscar; Iñiguez, Benjamín; García-Moreno, Eugeni; García, Rodolfo; Estrada, Magali
Precise extraction of transistor model parameters is of much importance for modeling and at the same time a difficult and time consuming task. Methods for parameter extraction can rely on purely mathematical basis, calling for intensive use of computational resources, or in human expertise to interpret results. In this work, we propose a method for parameter extraction based on fuzzy logic that includes a precise knowledge about the function of each parameter in the model to create a set of simple fitting rules that are easy to describe in human language. To simplify the computational effort, the parameter fitting rules work using only data at specific points (e.g. the distance between the calculated curve and the measured one at VDS corresponding to 50% of the maximum current). If necessary, a more accurate implementation can be used without altering the basic underlying philosophy of the method. In this work, the method is applied to extract model parameters required by Level 3 bulk MOS model and by a compact model for TFTs used in the Unified Model and Extraction Method (UMEM), which is based on an integral function. Results obtained show that the method is quite insensitive to the initial conditions and that it is also quite fast. Extension of this method for more complex models requires only the creation of the corresponding rule base, using the appropriate measurements. The method is especially useful for production testing or design.
A Fuzzy Description Logic with Automatic Object Membership Measurement
NASA Astrophysics Data System (ADS)
Cai, Yi; Leung, Ho-Fung
In this paper, we propose a fuzzy description logic named f om -DL by combining the classical view in cognitive psychology and fuzzy set theory. A formal mechanism used to determine object memberships automatically in concepts is also proposed, which is lacked in previous work fuzzy description logics. In this mechanism, object membership is based on the defining properties of concept definition and properties in object description. Moreover, while previous works cannot express the qualitative measurements of an object possessing a property, we introduce two kinds of properties named N-property and L-property, which are quantitative measurements and qualitative measurements of an object possessing a property respectively. The subsumption and implication of concepts and properties are also explored in our work. We believe that it is useful to the Semantic Web community for reasoning the fuzzy membership of objects for concepts in fuzzy ontologies.
Seismic event interpretation using fuzzy logic and neural networks
Maurer, W.J.; Dowla, F.U.
In the computer interpretation of seismic data, unknown sources of seismic events must be represented and reasoned about using measurements from the recorded signal. In this report, we develop the use of fuzzy logic to improve our ability to interpret weak seismic events. Processing strategies for the use of fuzzy set theory to represent vagueness and uncertainty, a phenomena common in seismic data analysis, are developed. A fuzzy-assumption based truth-maintenance-inferencing engine is also developed. Preliminary results in interpreting seismic events using the fuzzy neural network knowledge-based system are presented.
Life insurance risk assessment using a fuzzy logic expert system
NASA Technical Reports Server (NTRS)
Carreno, Luis A.; Steel, Roy A.
In this paper, we present a knowledge based system that combines fuzzy processing with rule-based processing to form an improved decision aid for evaluating risk for life insurance. This application illustrates the use of FuzzyCLIPS to build a knowledge based decision support system possessing fuzzy components to improve user interactions and KBS performance. The results employing FuzzyCLIPS are compared with the results obtained from the solution of the problem using traditional numerical equations. The design of the fuzzy solution consists of a CLIPS rule-based system for some factors combined with fuzzy logic rules for others. This paper describes the problem, proposes a solution, presents the results, and provides a sample output of the software product.
Wastewater neutralization control based in fuzzy logic: Simulation results
Garrido, R.; Adroer, M.; Poch, M.
Neutralization is a technique widely used as a part of wastewater treatment processes. Due to the importance of this technique, extensive study has been devoted to its control. However, industrial wastewater neutralization control is a procedure with a lot of problems--nonlinearity of the titration curve, variable buffering, changes in loading--and despite the efforts devoted to this subject, the problem has not been totally solved. in this paper, the authors present the development of a controller based in fuzzy logic (FLC). In order to study its effectiveness, it has been compared, by simulation, with other advanced controllers (using identification techniques and adaptive control algorithms using reference models) when faced with various types of wastewater with different buffer capacity or when changes in the concentration of the acid present in the wastewater take place. Results obtained show that FLC could be considered as a powerful alternative for wastewater neutralization processes.
Navigating a Mobile Robot Across Terrain Using Fuzzy Logic
NASA Technical Reports Server (NTRS)
Seraji, Homayoun; Howard, Ayanna; Bon, Bruce
A strategy for autonomous navigation of a robotic vehicle across hazardous terrain involves the use of a measure of traversability of terrain within a fuzzy-logic conceptual framework. This navigation strategy requires no a priori information about the environment. Fuzzy logic was selected as a basic element of this strategy because it provides a formal methodology for representing and implementing a human driver s heuristic knowledge and operational experience. Within a fuzzy-logic framework, the attributes of human reasoning and decision- making can be formulated by simple IF (antecedent), THEN (consequent) rules coupled with easily understandable and natural linguistic representations. The linguistic values in the rule antecedents convey the imprecision associated with measurements taken by sensors onboard a mobile robot, while the linguistic values in the rule consequents represent the vagueness inherent in the reasoning processes to generate the control actions. The operational strategies of the human expert driver can be transferred, via fuzzy logic, to a robot-navigation strategy in the form of a set of simple conditional statements composed of linguistic variables. These linguistic variables are defined by fuzzy sets in accordance with user-defined membership functions. The main advantages of a fuzzy navigation strategy lie in the ability to extract heuristic rules from human experience and to obviate the need for an analytical model of the robot navigation process.
Fuzzy logic, neural networks, and soft computing
NASA Technical Reports Server (NTRS)
Zadeh, Lofti A.
The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial
Fuzzy logic control and optimization system
Lou, Xinsheng
A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
Adaptive control of redundant multilink robot using fuzzy logic
NASA Astrophysics Data System (ADS)
Su, X.; Mitra, Sunanda
A new approach to fuzzy distance and restriction measures is used to obtain the appropriate orientations of the links for avoiding obstacles in the robot trajectories. This approach eliminates the classical task of solving highly coupled, nonlinear equations describing the ill- posed inverse problems of multilink robot motion at a much less demanding computational time. Such clear advantage of fuzzy logic based adaptive controller are illustrated by simulation results of guidance of a multilink robot in target positioning and trajectories tracking. The simulation results involve a three-link robot arm with capability of moving from one position to any desired position and tracking a defined trajectories accurately. A modified fuzzy rule based distance measure allows the robot to follow trajectories within hitting the obstacles in the path. The simulation results indicate the advantage of fuzzy logic based adaptive controllers in multiple criteria decision-making tasks.
Fuzzy logic applications to expert systems and control
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Jani, Yashvant
A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings.
The paper discusses an EPA program investigating fuzzy logic motor control for improved pollution prevention and energy efficiency. nitial computer simulation and laboratory results have demonstrated that fuzzy logic energy optimizers can consistently improve motor operational ef...
Probing Dynamical Character of Neural Circuits by Using Fuzzy Logic
NASA Astrophysics Data System (ADS)
Hu, Hong; Shi, Zhongzhi
Analytical study or designing of large-scale nonlinear neural circuits, especially for chaotic neural circuits, is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells' dynamical equations. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system, and we proved that if a neural system works in a non-chaotic way, a suitable fuzzy logical framework can be found and we can analyze or design such kind neural system similar to analyze or design a digit computer, but if a neural system works in a chaotic way, an approximation is needed for understanding the function of such neural system.
Design and performance comparison of fuzzy logic based tracking controllers
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Jani, Yashvant
Several camera tracking controllers based on fuzzy logic principles have been designed and tested in software simulation in the software technology branch at the Johnson Space Center. The fuzzy logic based controllers utilize range measurement and pixel positions from the image as input parameters and provide pan and tilt gimble rate commands as output. Two designs of the rulebase and tuning process applied to the membership functions are discussed in light of optimizing performance. Seven test cases have been designed to test the performance of the controllers for proximity operations where approaches like v-bar, fly-around and station keeping are performed. The controllers are compared in terms of responsiveness, and ability to maintain the object in the field-of-view of the camera. Advantages of the fuzzy logic approach with respect to the conventional approach have been discussed in terms of simplicity and robustness.
Experiments on neural network architectures for fuzzy logic
NASA Technical Reports Server (NTRS)
Keller, James M.
The use of fuzzy logic to model and manage uncertainty in a rule-based system places high computational demands on an inference engine. In an earlier paper, the authors introduced a trainable neural network structure for fuzzy logic. These networks can learn and extrapolate complex relationships between possibility distributions for the antecedents and consequents in the rules. Here, the power of these networks is further explored. The insensitivity of the output to noisy input distributions (which are likely if the clauses are generated from real data) is demonstrated as well as the ability of the networks to internalize multiple conjunctive clause and disjunctive clause rules. Since different rules with the same variables can be encoded in a single network, this approach to fuzzy logic inference provides a natural mechanism for rule conflict resolution.
Completed Optimised Structure of Threonine Molecule by Fuzzy Logic Modelling
NASA Astrophysics Data System (ADS)
Sahiner, Ahmet; Ucun, Fatih; Kapusuz, Gulden; Yilmaz, Nurullah
In this study we applied the fuzzy logic approach in order to model the energy depending on the two torsion angles for the threonine (C4H9NO3) molecule. The model is set up according to theoretical results obtained by the density functional theory (B3LYP) with a 6-31 G(d) basic set on a Gausian program. We aimed to determine the best torsion angle values providing the energy of the molecule minimum by a fuzzy logic approach and to compare them with the density functional theory results. It was concluded that the fuzzy logic approach gives information about the untested data and its best value which are expensive and time-consuming to obtain by other methods and experimentation.
The design of thermoelectric footwear heating system via fuzzy logic.
Işik, Hakan; Saraçoğlu, Esra
In this study, Heat Control of Thermoelectric Footwear System via Fuzzy Logic has been implemented in order to use efficiently in cold weather conditions. Temperature control is very important in domestic as well as in many industrial applications. The final product is seriously affected from the changes in temperature. So it is necessary to reach some desired temperature points quickly and avoid large overshoot. Here, fuzzy logic acts an important role. PIC 16F877 microcontroller has been designed to act as fuzzy logic controller. The designed system provides energy saving and has better performance than proportional control that was implemented in the previous study. The designed system takes into consideration so appropriate parameters that it can also be applied to the people safely who has illnesses like diabetes, etc. PMID:18041286
Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories
NASA Technical Reports Server (NTRS)
Burchett, Bradley T.
The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.
Applications of fuzzy logic to control and decision making
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Jani, Yashvant
Long range space missions will require high operational efficiency as well as autonomy to enhance the effectivity of performance. Fuzzy logic technology has been shown to be powerful and robust in interpreting imprecise measurements and generating appropriate control decisions for many space operations. Several applications are underway, studying the fuzzy logic approach to solving control and decision making problems. Fuzzy logic algorithms for relative motion and attitude control have been developed and demonstrated for proximity operations. Based on this experience, motion control algorithms that include obstacle avoidance were developed for a Mars Rover prototype for maneuvering during the sample collection process. A concept of an intelligent sensor system that can identify objects and track them continuously and learn from its environment is under development to support traffic management and proximity operations around the Space Station Freedom. For safe and reliable operation of Lunar/Mars based crew quarters, high speed controllers with ability to combine imprecise measurements from several sensors is required. A fuzzy logic approach that uses high speed fuzzy hardware chips is being studied.
Coordinated signal control for arterial intersections using fuzzy logic
NASA Astrophysics Data System (ADS)
Kermanian, Davood; Zare, Assef; Balochian, Saeed
Every day growth of the vehicles has become one of the biggest problems of urbanism especially in major cities. This can waste people's time, increase the fuel consumption, air pollution, and increase the density of cars and vehicles. Fuzzy controllers have been widely used in many consumer products and industrial applications with success over the past two decades. This article proposes a comprehensive model of urban traffic network using state space equations and then using Fuzzy Logic Tool Box and SIMULINK Program MATLAB a fuzzy controller in order to optimize and coordinate signal control at two intersections at an arterial road. The fuzzy controller decides to extend, early cut or terminate a signal phase and phase sequence to ensure smooth flow of traffic with minimal waiting time and length of queue. Results show that the performance of the proposed traffic controller at novel fuzzy model is better that of conventional controllers under normal and abnormal traffic conditions.
Fuzzy logic control synthesis without any rule base.
Novakovic, B M
A new analytic fuzzy logic control (FLC) system synthesis without any rule base is proposed. For this purpose the following objectives are preferred and reached: 1) an introduction of a new adaptive shape of fuzzy sets and a new adaptive distribution of input fuzzy sets, 2) a determination of an analytic activation function for activation of output fuzzy sets, instead of using of min-max operators, and 3) a definition of a new analytic function that determines the positions of centers of output fuzzy sets in each mapping process, instead of definition of the rule base. A real capability of the proposed FLC synthesis procedures is presented by synthesis of FLC of robot of RRTR-structure. PMID:18252321
Concurrent engineering use of fuzzy logic
Feraille, F.; Chedmail, P.
In concurrent engineering a crucial problem is the management of conflicts. To avoid conflicts between the different viewpoints and activities, the usual tools and methods are rather poor. As we can group all the constraints from several viewpoints, and solve the global problem. But it`s difficult to manage the associated mathematical problem. However as proposed, every designer who is acting at the design of the product solves his own problem, the different solutions are collected. But in this case, we can`t avoid clash or conflicts. Therefore we propose a new approach including fuzzy design environment. On an example, we present a fuzzy environment approach to design in a concurrent engineering context. So we can reduce clashes between viewpoints. After this, we present the tools we need to design with fuzzy variables. First, we summarize the usual method to optimize a problem with fuzzy constraints or parameters, giving an usual solution vector. Secondly, we introduce the concept of fuzzy solutions-set of an optimization problem. This concept is a generalization of the {open_quotes}solution family{close_quotes} notion as proposed. Ours is stronger because we attach to the solution vector X a satisfaction function {mu}(X) which mathematically describes a fuzzy solution set. We also propose a method to obtain such sets. Third, we present how to collect the different fuzzy solutions-sets from different viewpoints, in order to obtain the global fuzzy solutions-set of a design. We must pay attention to the fact that two viewpoints may have several common variables. Finally we describe the exploration of this global fuzzy solutions-set by different viewpoints using such variables as parameters for a new optimization of concurrent engineering.
The paper discusses EPA's research program to develop fuzzy-logic-based energy optimizers for alternating-current (AC) induction motors driven by Adjustable Speed Drives (ASDs). he technical goals of the program are to increase the efficiency of ASD/motor combinations (especially...
Professional Learning: A Fuzzy Logic-Based Modelling Approach
ERIC Educational Resources Information Center
Gravani, M. N.; Hadjileontiadou, S. J.; Nikolaidou, G. N.; Hadjileontiadis, L. J.
Studies have suggested that professional learning is influenced by two key parameters, i.e., climate and planning, and their associated variables (mutual respect, collaboration, mutual trust, supportiveness, openness). In this paper, we applied analysis of the relationships between the proposed quantitative, fuzzy logic-based model and a series of…
Fuzzy logic control of the building structure with CLEMR dampers
NASA Astrophysics Data System (ADS)
Zhang, Xiang-Cheng; Xu, Zhao-Dong; Huang, Xing-Huai; Zhu, Jun-Tao
The semi-active control technology has been paid more attention in the field of structural vibration control due to its high controllability, excellent control effect and low power requirement. When semi-active control device are used for vibration control, some challenges must be taken into account, such as the reliability and the control strategy of the device. This study presents a new large tonnage compound lead extrusion magnetorheological (CLEMR) damper, whose mathematical model is introduced to describe the variation of damping force with current and velocity. Then a current controller based on the fuzzy logic control strategy is designed to determine control currents of the CLEMR dampers rapidly. A ten-floor frame structure with CLEMR dampers using the fuzzy logic control strategy is built and calculated by using MATLAB. Calculation results show that CLEMR dampers can reduce the seismic responses of structures effectively. Calculation results of the fuzzy logic control strategy are compared with those of the semi-active limit Hrovat control structure, the passive-off control structure, and the uncontrolled structure. Comparison results show that the fuzzy logic control strategy can determine control currents of CLEMR dampers quickly and can reduce seismic responses of the structures more effectively than the passive-off control strategy and the uncontrolled structure.
Autonomous vehicle motion control, approximate maps, and fuzzy logic
NASA Technical Reports Server (NTRS)
Ruspini, Enrique H.
Progress on research on the control of actions of autonomous mobile agents using fuzzy logic is presented. The innovations described encompass theoretical and applied developments. At the theoretical level, results of research leading to the combined utilization of conventional artificial planning techniques with fuzzy logic approaches for the control of local motion and perception actions are presented. Also formulations of dynamic programming approaches to optimal control in the context of the analysis of approximate models of the real world are examined. Also a new approach to goal conflict resolution that does not require specification of numerical values representing relative goal importance is reviewed. Applied developments include the introduction of the notion of approximate map. A fuzzy relational database structure for the representation of vague and imprecise information about the robot's environment is proposed. Also the central notions of control point and control structure are discussed.
An Innovative Fuzzy-Logic-Based Methodology for Trend Identification
Wang Xin; Tsoukalas, Lefteri H.; Wei, Thomas Y.C.; Reifman, Jaques
A new fuzzy-logic-based methodology for on-line signal trend identification is introduced. The methodology may be used for detecting the onset of nuclear power plant (NPP) transients at the earliest possible time and could be of great benefit to diagnostic, maintenance, and performance-monitoring programs. Although signal trend identification is complicated by the presence of noise, fuzzy methods can help capture important features of on-line signals, integrate the information included in these features, and classify incoming NPP signals into increasing, decreasing, and steady-state trend categories. A computer program named PROTREN is developed and tested for the purpose of verifying this methodology using NPP and simulation data. The results indicate that the new fuzzy-logic-based methodology is capable of detecting transients accurately, it identifies trends reliably and does not misinterpret a steady-state signal as a transient one.
North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2
NASA Technical Reports Server (NTRS)
Villarreal, James A. (Compiler)
This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies.
North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 1
NASA Technical Reports Server (NTRS)
Villarreal, James A. (Compiler)
This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such as neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies.
Stock and option portfolio using fuzzy logic approach
NASA Astrophysics Data System (ADS)
Sumarti, Novriana; Wahyudi, Nanang
Fuzzy Logic in decision-making process has been widely implemented in various problems in industries. It is the theory of imprecision and uncertainty that was not based on probability theory. Fuzzy Logic adds values of degree between absolute true and absolute false. It starts with and builds on a set of human language rules supplied by the user. The fuzzy systems convert these rules to their mathematical equivalents. This could simplify the job of the system designer and the computer, and results in much more accurate representations of the way systems behave in the real world. In this paper we examine the decision making process of stock and option trading by the usage of MACD (Moving Average Convergence Divergence) technical analysis and Option Pricing with Fuzzy Logic approach. MACD technical analysis is for the prediction of the trends of underlying stock prices, such as bearish (going downward), bullish (going upward), and sideways. By using Fuzzy C-Means technique and Mamdani Fuzzy Inference System, we define the decision output where the value of MACD is high then decision is "Strong Sell", and the value of MACD is Low then the decision is "Strong Buy". We also implement the fuzzification of the Black-Scholes option-pricing formula. The stock and options methods are implemented on a portfolio of one stock and its options. Even though the values of input data, such as interest rates, stock price and its volatility, cannot be obtain accurately, these fuzzy methods can give a belief degree of the calculated the Black-Scholes formula so we can make the decision on option trading. The results show the good capability of the methods in the prediction of stock price trends. The performance of the simulated portfolio for a particular period of time also shows good return.
Approach to Synchronization Control of Magnetic Bearings Using Fuzzy Logic
NASA Technical Reports Server (NTRS)
Yang, Li-Farn
This paper presents a fuzzy-logic approach to the synthesis of synchronization control for magnetically suspended rotor system. The synchronization control enables a whirling rotor to undergo synchronous motion along the magnetic bearing axes; thereby avoiding the gyroscopic effect that degrade the stability of rotor systems when spinning at high speed. The control system features a fuzzy controller acting on the magnetic bearing device, in which the fuzzy inference system trained through fuzzy rules to minimize the differential errors between four bearing axes so that an error along one bearing axis can affect the overall control loop for the motion synchronization. Numerical simulations of synchronization control for the magnetically suspended rotor system are presented to show the effectiveness of the present approach.
Boolean and fuzzy logic implemented at the molecular level
NASA Astrophysics Data System (ADS)
Gentili, Pier Luigi
In this work, it is shown how to implement both hard and soft computing by means of two structurally related heterocyclic compounds: flindersine (FL) and 6(5H)-phenanthridinone (PH). Since FL and PH have a carbonyl group in their molecular skeletons, they exhibit Proximity Effects in their photophysics. In other words, they have an emission power that can be modulated through external inputs such as temperature ( T) and hydrogen-bonding donation (HBD) ability of solvents. This phenomenology can be exploited to implement both crisp and fuzzy logic. Fuzzy Logic Systems (FLSs) wherein the antecedents of the rules are connected through the AND operator, are built by both the Mamdani's and Sugeno's models. Finally, they are adopted as approximators of the proximity effect phenomenon and tested for their prediction capabilities. Moreover, FL as photochromic compound is also a multiply configurable crisp logic molecular element.
Modal control of a plate using a fuzzy logic controller
NASA Astrophysics Data System (ADS)
Sharma, Manu; Singh, S. P.; Sachdeva, B. L.
This paper presents fuzzy logic based independent modal space control (IMSC) and fuzzy logic based modified independent modal space control (MIMSC) of vibration. The rule base of the controller consists of nine rules, which have been derived based upon simple human reasoning. Input to the controller consists of the first two modal displacements and velocities of the structure and the output of the controller is the modal force to be applied by the actuator. Fuzzy logic is used in such a way that the actuator is never called to apply effort which is beyond safe limits and also the operator is saved from calculating control gains. The proposed fuzzy controller is experimentally tested for active vibration control of a cantilevered plate. A piezoelectric patch is used as a sensor to sense vibrations of the plate and another piezoelectric patch is used as an actuator to control vibrations of the plate. For analytical formulation, a finite element method based upon Hamilton's principle is used to model the plate. For experimentation, the first two modes of the plate are observed using a Kalman observer. Real-time experiments are performed to control the first mode, the second mode and both modes simultaneously. Experiments are also performed to control the first mode by IMSC, the second mode by IMSC and both modes simultaneously by MIMSC. It is found that for the same decibel reduction in the first mode, the voltage applied by the fuzzy logic based controller is less than that applied by IMSC. While controlling the second mode by IMSC, a considerable amount of spillover is observed in the first mode and region just after the second mode, whereas while controlling the second mode by fuzzy logic, spillover effects are much smaller. While controlling two modes simultaneously, with a single sensor/actuator pair, appreciable resonance control is observed both with fuzzy logic based MIMSC as well as with direct MIMSC, but there is a considerable amount of spillover in the off
Fuzzy Logic Decoupled Lateral Control for General Aviation Airplanes
NASA Technical Reports Server (NTRS)
Duerksen, Noel
It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control different airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control aileron or roll spoiler position. This controller was used to control bank angle for both a piston powered single engine aileron equipped airplane simulation and a business jet simulation which used spoilers for primary roll control. Overspeed, stall and overbank protection were incorporated in the form of expert systems supervisors and weighted fuzzy rules. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic lateral controller could be successfully used on two general aviation aircraft types that have very different characteristics. These controllers worked for both airplanes over their entire flight envelopes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle ]ever travel, etc.). This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.
Automatic control of pressure support mechanical ventilation using fuzzy logic.
Nemoto, T; Hatzakis, G E; Thorpe, C W; Olivenstein, R; Dial, S; Bates, J H
There is currently no universally accepted approach to weaning patients from mechanical ventilation, but there is clearly a feeling within the medical community that it may be possible to formulate the weaning process algorithmically in some manner. Fuzzy logic seems suited this task because of the way it so naturally represents the subjective human notions employed in much of medical decision-making. The purpose of the present study was to develop a fuzzy logic algorithm for controlling pressure support ventilation in patients in the intensive care unit, utilizing measurements of heart rate, tidal volume, breathing frequency, and arterial oxygen saturation. In this report we describe the fuzzy logic algorithm, and demonstrate its use retrospectively in 13 patients with severe chronic obstructive pulmonary disease, by comparing the decisions made by the algorithm with what actually transpired. The fuzzy logic recommendations agreed with the status quo to within 2 cm H(2)O an average of 76% of the time, and to within 4 cm H(2)O an average of 88% of the time (although in most of these instances no medical decisions were taken as to whether or not to change the level of ventilatory support). We also compared the predictions of our algorithm with those cases in which changes in pressure support level were actually made by an attending physician, and found that the physicians tended to reduce the support level somewhat more aggressively than the algorithm did. We conclude that our fuzzy algorithm has the potential to control the level of pressure support ventilation from ongoing measurements of a patient's vital signs. PMID:10430727
Content-addressable-memory for the three key operations of fuzzy logic
NASA Astrophysics Data System (ADS)
Jiang, Tao; Li, Yao
Today, most fuzzy logic operations are performed via software means, which is inevitably slow. While searching for long term hardware solutions to realize analog fuzzy logic operations, the use of the well-developed Boolean logic hardware with analog to digital and digital to analog converters to implement the digitized fuzzy logic could provide an efficient solution. Similar to Boolean logic, digitized fuzzy logic operations can be written as a minimized sum-of-product term format, which can then be implemented based on programmable logic arrays. We address a fundamental issue of the computational complexity of this method. We derive the minimum number of the Boolean sum-of-product terms for some key fuzzy logic operations, such as Union, Intersection, and Complement operators. Our derivations provide ways to estimate the general computational complexity or memory capacity of using binary circuits, electronic or optoelectronic, to implement the digitized analog logic operations.
A Numerical Optimization Approach for Tuning Fuzzy Logic Controllers
NASA Technical Reports Server (NTRS)
Woodard, Stanley E.; Garg, Devendra P.
This paper develops a method to tune fuzzy controllers using numerical optimization. The main attribute of this approach is that it allows fuzzy logic controllers to be tuned to achieve global performance requirements. Furthermore, this approach allows design constraints to be implemented during the tuning process. The method tunes the controller by parameterizing the membership functions for error, change-in-error and control output. The resulting parameters form a design vector which is iteratively changed to minimize an objective function. The minimal objective function results in an optimal performance of the system. A spacecraft mounted science instrument line-of-sight pointing control is used to demonstrate results.
Outstanding-objects-oriented color image segmentation using fuzzy logic
NASA Astrophysics Data System (ADS)
Hayasaka, Rina; Zhao, Jiying; Matsushita, Yutaka
This paper presents a novel fuzzy-logic-based color image segmentation scheme focusing on outstanding objects to human eyes. The scheme first segments the image into rough fuzzy regions, chooses visually significant regions, and conducts fine segmentation on the chosen regions. It can not only reduce the computational load, but also make contour detection easy because the brief object externals has been previously determined. The scheme reflects human sense, and it can be sued efficiently in automatic extraction of image retrieval key, robot vision and region-adaptive image compression.
An Analysis Regarding the Possibility of Using Fuzzy Logic in Inventory Management
NASA Astrophysics Data System (ADS)
Stoia, Claudiu-Leonardo
The paper presents a brief state-of-the-art survey regarding the use of fuzzy logic in inventory management. Its goal is to motivate enthusiastic entrepreneurs to take into account the benefits of using fuzzy logic inventory control systems. It offers a guide to model an inventory system having a free fuzzy tool as starting point
Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2
NASA Technical Reports Server (NTRS)
Lea, Robert N. (Editor); Villarreal, James A. (Editor)
Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making.
Vector control and fuzzy logic control of doubly fed variable speed drives with DSP implementation
Tang, Y.; Xu, L.
Field orientation control and fuzzy logic control are designed for variable speed drive systems with a doubly fed machine in slip power recovery configuration. Laboratory implementation with a general purpose DSP (digital signal processing) system is described and experimental results are given. High performance potential of a slip power recovery system is realized with these advanced controls, while flexible reactive power control becomes possible, and compared to the ordinary variable speed drives with singly fed induction machine, power converter rating is reduced.
Fuzzy logic control system to provide autonomous collision avoidance for Mars rover vehicle
NASA Technical Reports Server (NTRS)
Murphy, Michael G.
NASA is currently involved with planning unmanned missions to Mars to investigate the terrain and process soil samples in advance of a manned mission. A key issue involved in unmanned surface exploration on Mars is that of supporting autonomous maneuvering since radio communication involves lengthy delays. It is anticipated that specific target locations will be designated for sample gathering. In maneuvering autonomously from a starting position to a target position, the rover will need to avoid a variety of obstacles such as boulders or troughs that may block the shortest path to the target. The physical integrity of the rover needs to be maintained while minimizing the time and distance required to attain the target position. Fuzzy logic lends itself well to building reliable control systems that function in the presence of uncertainty or ambiguity. The following major issues are discussed: (1) the nature of fuzzy logic control systems and software tools to implement them; (2) collision avoidance in the presence of fuzzy parameters; and (3) techniques for adaptation in fuzzy logic control systems.
Use of Fuzzy Logic Systems for Assessment of Primary Faults
NASA Astrophysics Data System (ADS)
Petrović, Ivica; Jozsa, Lajos; Baus, Zoran
In electric power systems, grid elements are often subjected to very complex and demanding disturbances or dangerous operating conditions. Determining initial fault or cause of those states is a difficult task. When fault occurs, often it is an imperative to disconnect affected grid element from the grid. This paper contains an overview of possibilities for using fuzzy logic in an assessment of primary faults in the transmission grid. The tool for this task is SCADA system, which is based on information of currents, voltages, events of protection devices and status of circuit breakers in the grid. The function model described with the membership function and fuzzy logic systems will be presented in the paper. For input data, diagnostics system uses information of protection devices tripping, states of circuit breakers and measurements of currents and voltages before and after faults.
Control of a flexible beam using fuzzy logic
NASA Technical Reports Server (NTRS)
Mccullough, Claire L.
The goal of this project, funded under the NASA Summer Faculty Fellowship program, was to evaluate control methods utilizing fuzzy logic for applicability to control of flexible structures. This was done by applying these methods to control of the Control Structures Interaction Suitcase Demonstrator developed at Marshall Space Flight Center. The CSI Suitcase Demonstrator is a flexible beam, mounted at one end with springs and bearing, and with a single actuator capable of rotating the beam about a pin at the fixed end. The control objective is to return the tip of the free end to a zero error position (from a nonzero initial condition). It is neither completely controllable nor completely observable. Fuzzy logic control was demonstrated to successfully control the system and to exhibit desirable robustness properties compared to conventional control.
Fuzzy temporal logic based railway passenger flow forecast model.
Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun
Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586
Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model
Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun
Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586
Intelligent control based on fuzzy logic and neural net theory
NASA Technical Reports Server (NTRS)
Lee, Chuen-Chien
In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.
Optimizing transverse particle distributions in the accumulator ring is one of most important factors to the future performance of the Spallation Neutron Source (SNS) [l]. This can only be achieved by optimizing the injection bumps that paint the beam in phase space. The process is complex due to the vague distribution inputs and the multiple optimization goals. Furthermore, the priority of the optimization criteria could change at different operational stages. We propose optimizing transverse phase space painting with fuzzy logic and present our initial studies toward that end. The focus of this paper is on how the problem can be solved with a Fuzzy Logic (FL) expert system through the creation of a set of rules that can be applied by the system. Various particle distributions, from computer simulations, are analyzed with FL and the results are compared and discussed. Finally, a run-time optimization control system is proposed.
An architecture for designing fuzzy logic controllers using neural networks
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.
Rule based fuzzy logic approach for classification of fibromyalgia syndrome.
Arslan, Evren; Yildiz, Sedat; Albayrak, Yalcin; Koklukaya, Etem
Fibromyalgia syndrome (FMS) is a chronic muscle and skeletal system disease observed generally in women, manifesting itself with a widespread pain and impairing the individual's quality of life. FMS diagnosis is made based on the American College of Rheumatology (ACR) criteria. However, recently the employability and sufficiency of ACR criteria are under debate. In this context, several evaluation methods, including clinical evaluation methods were proposed by researchers. Accordingly, ACR had to update their criteria announced back in 1990, 2010 and 2011. Proposed rule based fuzzy logic method aims to evaluate FMS at a different angle as well. This method contains a rule base derived from the 1990 ACR criteria and the individual experiences of specialists. The study was conducted using the data collected from 60 inpatient and 30 healthy volunteers. Several tests and physical examination were administered to the participants. The fuzzy logic rule base was structured using the parameters of tender point count, chronic widespread pain period, pain severity, fatigue severity and sleep disturbance level, which were deemed important in FMS diagnosis. It has been observed that generally fuzzy predictor was 95.56 % consistent with at least of the specialists, who are not a creator of the fuzzy rule base. Thus, in diagnosis classification where the severity of FMS was classified as well, consistent findings were obtained from the comparison of interpretations and experiences of specialists and the fuzzy logic approach. The study proposes a rule base, which could eliminate the shortcomings of 1990 ACR criteria during the FMS evaluation process. Furthermore, the proposed method presents a classification on the severity of the disease, which was not available with the ACR criteria. The study was not limited to only disease classification but at the same time the probability of occurrence and severity was classified. In addition, those who were not suffering from FMS were
Design and implementation of fuzzy logic controllers. Thesis Final Report, 27 Jul. 1992 - 1 Jan. 1993
NASA Technical Reports Server (NTRS)
Abihana, Osama A.; Gonzalez, Oscar R.
The main objectives of our research are to present a self-contained overview of fuzzy sets and fuzzy logic, develop a methodology for control system design using fuzzy logic controllers, and to design and implement a fuzzy logic controller for a real system. We first present the fundamental concepts of fuzzy sets and fuzzy logic. Fuzzy sets and basic fuzzy operations are defined. In addition, for control systems, it is important to understand the concepts of linguistic values, term sets, fuzzy rule base, inference methods, and defuzzification methods. Second, we introduce a four-step fuzzy logic control system design procedure. The design procedure is illustrated via four examples, showing the capabilities and robustness of fuzzy logic control systems. This is followed by a tuning procedure that we developed from our design experience. Third, we present two Lyapunov based techniques for stability analysis. Finally, we present our design and implementation of a fuzzy logic controller for a linear actuator to be used to control the direction of the Free Flight Rotorcraft Research Vehicle at LaRC.
A robust fuzzy logic controller for robot manipulators with uncertainties.
Yi, S Y; Chung, M J
Owing to load variation and unmodeled dynamics, a robot manipulator can be classified as a nonlinear dynamic system with structured and unstructured uncertainties. In this paper, the stability and robustness of a class of the fuzzy logic control (FLC) is investigated and a robust FLC is proposed for a robot manipulator with uncertainties. In order to show the performance of the proposed control algorithm, computer simulations are carried out on a simple two-link robot manipulator. PMID:18255910
Optimized Reactive Power Compensation Using Fuzzy Logic Controller
NASA Astrophysics Data System (ADS)
George, S.; Mini, K. N.; Supriya, K.
Reactive power flow in a long transmission line plays a vital role in power transfer capability and voltage stability in power system. Traditionally, shunt connected compensators are used to control reactive power in long transmission line. Thyristor controlled reactor is used to control reactive power under lightly loaded condition. By controlling firing angle of thyristor, it is possible to control reactive power in the transmission lines. However, thyristor controlled reactor will inject harmonic current into the system. An attempt to reduce reactive power injection will increase harmonic distortion in the line current and vice versa. Thus, there is a trade-off between reactive power injection and harmonics in current. By optimally controlling the reactive power injection, harmonics in current can be brought within the specified limit. In this paper, a Fuzzy Logic Controller is implemented to obtain optimal control of reactive power of the compensator to maintain voltage and harmonic in current within the limits. An algorithm which optimizes the firing angle in each fuzzy subset by calculating the rank of feasible firing angles is proposed for the construction of rules in Fuzzy Logic Controller. The novelty of the algorithm is that it uses a simple error formula for the calculation of the rank of the feasible firing angles in each fuzzy subset.
Learning and tuning fuzzy logic controllers through reinforcements
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.; Khedkar, Pratap
This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.
Learning and tuning fuzzy logic controllers through reinforcements
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.; Khedkar, Pratap
A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.
Fuzzy logic controls pressure in Fracturing Fluid Characterization Facility
Rivera, V.P.; Farabee, L.M.
A fuzzy logic pressure control system has been designed and implemented to deal with the demanding requirements of the Fracturing Fluid Characterization Facility (FFCF), a test bed that simulates downhole conditions for investigating fluid behavior during fracturing stimulation. Pressure control in the fracture simulator was difficult because of the wide range of fluid types and pumping conditions used and by the compliant structure of the simulator, which uses servo-controlled actuators to maintain a constant gap width under varying pressure conditions. The FFCF pressure control system must handle fluids that vary from water to high-viscosity gel slurries at flow rates ranging from 1/2 to 3 bbl/min. Conventional control approaches were successful only under very limited conditions. To solve this problem, a fuzzy logic controller (FLC) was developed to be a user function in the FFCF supervisory control and data acquisition system. Using several fuzzy logic rules, the FLC generates a position set point for a slurry throttling valve. An electro-hydraulic directional control valve uses the set point supplied by the FLC to position the active control element of the slurry throttling valve.
Fuzzy Logic Decoupled Longitudinal Control for General Aviation Airplanes
NASA Technical Reports Server (NTRS)
Duerksen, Noel
It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control difference airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control throttle position and another to control elevator position. These two controllers were used to control flight path angle and airspeed for both a piston powered single engine airplane simulation and a business jet simulation. Overspeed protection and stall protection were incorporated in the form of expert systems supervisors. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic longitudinal controller could be successfully used on two general aviation aircraft types that have very difference characteristics. These controllers worked for both airplanes over their entire flight envelopes including configuration changes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle lever travel, etc.). The controllers also handled configuration changes without mode switching or knowledge of the current configuration. This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.
Control Law for Automatic Landing Using Fuzzy-Logic Control
NASA Astrophysics Data System (ADS)
Kato, Akio; Inagaki, Yoshiki
The effectiveness of a fuzzy-logic control law for automatically landing an aircraft that handles both the control to lead an aircraft from horizontal flight at an altitude of 500 meters to flight along the glide-path course near the runway, as well as the control to direct the aircraft to land smoothly on a runway, was investigated. The control law for the automatic landing was designed to match the design goals of directing an aircraft from horizontal flight to flight along a glide-path course quickly and smoothly, and for landing smoothly on a runway. The design of the control law and evaluation of the control performance were performed considering the ground effect at landing. As a result, it was confirmed that the design goals were achieved. Even if the characteristics of the aircraft change greatly, the proposed control law is able to maintain the control performance. Moreover, it was confirmed to be able to land an aircraft safely during air turbulence. The present paper indicates that fuzzy-logic control is an effective and flexible method when applied to the control law for automatic landing, and the design method of the control law using fuzzy-logic control was obtained.
Control Law for Automatic Landing Using Fuzzy Logic Control
NASA Astrophysics Data System (ADS)
Kato, Akio; Inagaki, Yoshiki
The effectiveness of fuzzy logic control law for automatic landing of aircraft, which cover both of control to lead aircraft from level flight at an altitude of 500m to the flight on the glide-path course near the runway and control for the aircraft to land smoothly on a runway, was studied. The control law of the automatic landing was designed to match the design goals of leading from the horizontal flight to the flight on the glide-path course quickly and smoothly and of landing smoothly on a runway. Because there is the ground effect at landing, design of control law and evaluation of control performance were done in consideration of the ground effect. As a result, it was confirmed that the design objective was achieved. Even if the characteristics of the plant changes greatly, this control law was able to maintain the control performance. Moreover, it was confirmed to be able to land safely when there was air turbulence. This paper shows that fuzzy logic control is an effective and flexible method when applied to control law for automatic landing and the design method of control law using fuzzy logic control was obtained.
Fuzzy Logic: A New Tool for the Analysis and Organization of International Business Communications.
ERIC Educational Resources Information Center
Sondak, Norman E.; Sondak, Eileen M.
Classical western logic, built on a foundation of true/false, yes/no, right/wrong statements, leads to many difficulties and inconsistencies in the logical analysis and organization of international business communications. This paper presents the basic principles of classical logic and of fuzzy logic, a type of logic developed to allow for…
Searching arousals: A fuzzy logic approach.
Chaparro-Vargas, Ramiro; Ahmed, Beena; Penzel, Thomas; Cvetkovic, Dean
This paper presents a computational approach to detect spontaneous, chin tension and limb movement-related arousals by estimating neuronal and muscular activity. Features extraction is carried out by Time Varying Autoregressive Moving Average (TVARMA) models and recursive particle filtering. Classification is performed by a fuzzy inference system with rule-based decision scheme based upon the AASM scoring rules. Our approach yielded two metrics: arousal density and arousal index to comply with standardised clinical benchmarking. The obtained statistics achieved error deviation around ±1.5 to ±30. These results showed that our system can differentiate amongst 3 different types of arousals, subject to inter-subject variability and up-to-date scoring references. PMID:26736862
Distributed traffic signal control using fuzzy logic
NASA Technical Reports Server (NTRS)
Chiu, Stephen
We present a distributed approach to traffic signal control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. Thus, the signal timing parameters evolve dynamically using only local information to improve traffic flow. This distributed approach provides for a fault-tolerant, highly responsive traffic management system. The signal timing at an intersection is defined by three parameters: cycle time, phase split, and offset. We use fuzzy decision rules to adjust these three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. We show the effectiveness of this method through simulation of the traffic flow in a network of controlled intersections.
Security risk assessment: applying the concepts of fuzzy logic.
Bajpai, Shailendra; Sachdeva, Anish; Gupta, J P
Chemical process industries (CPI) handling hazardous chemicals in bulk can be attractive targets for deliberate adversarial actions by terrorists, criminals and disgruntled employees. It is therefore imperative to have comprehensive security risk management programme including effective security risk assessment techniques. In an earlier work, it has been shown that security risk assessment can be done by conducting threat and vulnerability analysis or by developing Security Risk Factor Table (SRFT). HAZOP type vulnerability assessment sheets can be developed that are scenario based. In SRFT model, important security risk bearing factors such as location, ownership, visibility, inventory, etc., have been used. In this paper, the earlier developed SRFT model has been modified using the concepts of fuzzy logic. In the modified SRFT model, two linguistic fuzzy scales (three-point and four-point) are devised based on trapezoidal fuzzy numbers. Human subjectivity of different experts associated with previous SRFT model is tackled by mapping their scores to the newly devised fuzzy scale. Finally, the fuzzy score thus obtained is defuzzyfied to get the results. A test case of a refinery is used to explain the method and compared with the earlier work. PMID:19744788
An Adaptive Fuzzy-Logic Traffic Control System in Conditions of Saturated Transport Stream.
Yusupbekov, N R; Marakhimov, A R; Igamberdiev, H Z; Umarov, Sh X
This paper considers the problem of building adaptive fuzzy-logic traffic control systems (AFLTCS) to deal with information fuzziness and uncertainty in case of heavy traffic streams. Methods of formal description of traffic control on the crossroads based on fuzzy sets and fuzzy logic are proposed. This paper also provides efficient algorithms for implementing AFLTCS and develops the appropriate simulation models to test the efficiency of suggested approach. PMID:27517081
An Adaptive Fuzzy-Logic Traffic Control System in Conditions of Saturated Transport Stream
Marakhimov, A. R.; Igamberdiev, H. Z.; Umarov, Sh. X.
This paper considers the problem of building adaptive fuzzy-logic traffic control systems (AFLTCS) to deal with information fuzziness and uncertainty in case of heavy traffic streams. Methods of formal description of traffic control on the crossroads based on fuzzy sets and fuzzy logic are proposed. This paper also provides efficient algorithms for implementing AFLTCS and develops the appropriate simulation models to test the efficiency of suggested approach. PMID:27517081
Robustness of fuzzy logic power system stabilizers applied to multimachine power system
Hiyama, Takashi . Dept. of Electrical Engineering and Computer Science)
This paper investigates the robustness of fuzzy logic stabilizers using the information of speed and acceleration states of a study unit. The input signals are the real power output and/or the speed of the study unit. Non-linear simulations show the robustness of the fuzzy logic power system stabilizers. Experiments are also performed by using a micro-machine system. The results show the feasibility of proposed fuzzy logic stabilizer.
Fuzzy knowledge base construction through belief networks based on Lukasiewicz logic
NASA Technical Reports Server (NTRS)
Lara-Rosano, Felipe
In this paper, a procedure is proposed to build a fuzzy knowledge base founded on fuzzy belief networks and Lukasiewicz logic. Fuzzy procedures are developed to do the following: to assess the belief values of a consequent, in terms of the belief values of its logical antecedents and the belief value of the corresponding logical function; and to update belief values when new evidence is available.
Medical application of fuzzy logic: fuzzy patient state in arterial hypertension analysis
NASA Astrophysics Data System (ADS)
Blinowska, Aleksandra; Duckstein, Lucien
A few existing applications of fuzzy logic in medicine are briefly described and some potential applications are reviewed. The problem of classification of patient states and medical decision making is discussed more in detail and illustrated by the example of a fuzzy rule based model developed to elicit, analyze and reproduce the opinions of multiple medical experts in the case of arterial hypertension. The goal was to reproduce the average coded answers using an adequate fuzzy procedure, here a fuzzy rule. State categories and the initial set of experimental parameters were defined according to medical practice. The fuzzy set membership functions were then assessed for each parameter in each category and a small subset of representative and pertinent parameters selected for each question. The data were split into two sets of 50 patient files each, the calibration set and the validation set. Two evaluation criteria were used: the sum of squared deviations and the sum of deviations. Fuzzy rules were then sought that reproduced the target, which was the average coded answer. Only one fuzzy rule `and' appeared to be necessary to describe the patient state in a continuous way and to approach the target as closely as the majority of experts.
Application of fuzzy logic in computer-aided design of digital systems
NASA Astrophysics Data System (ADS)
Shragowitz, Eugene B.; Lee, Jun-Yong; Kang, Eric Q.
Application of fuzzy logic structures in computer-aided design (CAD) of electronic systems substantially improves quality of design solutions by providing designers with flexibility in formulating goals and selecting trade-offs. In addition, the following aspects of a design process are positively impacted by application of fuzzy logic: utilization of domain knowledge, interpretation of uncertainties in design data, and adaptation of design algorithms. We successfully applied fuzzy logic structures in conjunction with constructive and iterative algorithms for selecting of design solutions for different stages of the design process. We also introduced a fuzzy logic software development tool to be used in CAD applications.
An investigation into the application of fuzzy logic to well stimulation treatment design
Xiong, H.; Holditch, S.A.
Designing an optimal stimulation treatment for an oil or gas well is a complex procedure requiring in-depth knowledge and experience. This paper describes how fuzzy logic applies to stimulation design and clearly illustrates how to apply fuzzy logic theory. The paper also discusses the advantages and disadvantages of applying fuzzy logic to well stimulation design. Fuzzy logic evaluators can be applied to study, evaluate, and determine the best methods to improve productivity in oil and gas wells or injectivity in water wells. The approach can be extended to the solution of other problems associated with drilling, completing, and working over wells and with formation damage diagnosis.
On-line fuzzy logic control of tube bending
NASA Astrophysics Data System (ADS)
Lieh, Junghsen; Li, Wei Jie
This paper describes the simulation and on-line fuzzy logic control of tube bending. By combining elasticity and plasticity theories, a conventional model was developed. The results from simulation were compared with those obtained from testing. The experimental data reveal that there exists certain level of uncertainty and nonlinearity in tube bending, and its variation could be significant. To overcome this, a on-line fuzzy logic controller with self-tuning capabilities was designed. The advantages of this on-line system are (1) its computational requirement is simple in comparison with more algorithmic-based controllers, and (2) the system does not need prior knowledge of material characteristics. The device includes an AC motor, a servo controller, a forming mechanism, a 3D optical sensor, and a microprocessor. This automated bending machine adopts primary and secondary errors between the actual response and desired output to conduct on-line rule reasoning. Results from testing show that the spring back angle can be effectively compensated by the self- tuning fuzzy system in a real-time fashion.
Motion Control of the Soccer Robot Based on Fuzzy Logic
NASA Astrophysics Data System (ADS)
Coman, Daniela; Ionescu, Adela
Robot soccer is a challenging platform for multi-agent research, involving topics such as real-time image processing and control, robot path planning, obstacle avoidance and machine learning. The conventional robot control consists of methods for path generation and path following. When a robot moves away the estimated path, it must return immediately, and while doing so, the obstacle avoidance behavior and the effectiveness of such a path are not guaranteed. So, motion control is a difficult task, especially in real time and high speed control. This paper describes the use of fuzzy logic control for the low level motion of a soccer robot. Firstly, the modelling of the soccer robot is presented. The soccer robot based on MiroSoT Small Size league is a differential-drive mobile robot with non-slipping and pure-rolling. Then, the design of fuzzy controller is describes. Finally, the computer simulations in MATLAB Simulink show that proposed fuzzy logic controller works well.
Fuzzy Logic Approaches to Multi-Objective Decision-Making in Aerospace Applications
NASA Technical Reports Server (NTRS)
Hardy, Terry L.
Fuzzy logic allows for the quantitative representation of multi-objective decision-making problems which have vague or fuzzy objectives and parameters. As such, fuzzy logic approaches are well-suited to situations where alternatives must be assessed by using criteria that are subjective and of unequal importance. This paper presents an overview of fuzzy logic and provides sample applications from the aerospace industry. Applications include an evaluation of vendor proposals, an analysis of future space vehicle options, and the selection of a future space propulsion system. On the basis of the results provided in this study, fuzzy logic provides a unique perspective on the decision-making process, allowing the evaluator to assess the degree to which each option meets the evaluation criteria. Future decision-making should take full advantage of fuzzy logic methods to complement existing approaches in the selection of alternatives.
A reinforcement learning-based architecture for fuzzy logic control
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.
Intelligent Paging Based Mobile User Tracking Using Fuzzy Logic
NASA Astrophysics Data System (ADS)
Saha, Sajal; Dutta, Raju; Debnath, Soumen; Mukhopadhyay, Asish K.
In general, a mobile user travels in a predefined path that depends mostly on the user's characteristics. Thus, tracking the locations of a mobile user is one of the challenges for location management. In this paper, we introduce a movement pattern learning strategy system to track the user's movements using adaptive fuzzy logic. Our fuzzy inference system extracts patterns from the historical data record of the cell numbers along with the date and time stamp of the users occupying the cell. Implementation of this strategy has been evaluated with the real time user data which proves the efficiency and accuracy of the model. This mechanism not only reduces user location tracking costs, but also significantly decreases the call-loss rates and average paging delays.
Application of fuzzy logic in multicomponent analysis by optodes.
Wollenweber, M; Polster, J; Becker, T; Schmidt, H L
Fuzzy logic can be a useful tool for the determination of substrate concentrations applying optode arrays in combination with flow injection analysis, UV-VIS spectroscopy and kinetics. The transient diffuse reflectance spectra in the visible wavelength region from four optodes were evaluated to carry out the simultaneous determination of artificial mixtures of ampicillin and penicillin. The discrimination of the samples was achieved by changing the composition of the receptor gel and working pH. Different algorithms of pre-processing were applied on the data to reduce the spectral information to a few analytic-specific variables. These variables were used to develop the fuzzy model. After calibration the model was validated by an independent test data set. PMID:9451783
Fuzzy logic sliding mode control for command guidance law design.
Elhalwagy, Y Z; Tarbouchi, M
Recently, the combination of sliding mode and fuzzy logic techniques has emerged as a promising methodology for dealing with nonlinear, uncertain, dynamical systems. In this paper, a sliding mode control algorithm combined with a fuzzy control scheme is developed for the trajectory control of a command guidance system. The acceleration command input is mathematically derived. The proposed controller is used to compensate for the influence of unmodeled dynamics and to alleviate chattering. Simulation results show that the proposed controller gives good system performance in the face of system parameters variation and external disturbances. In addition, they show the effectiveness of the proposed missile guidance law against different engagement scenarios where the results demonstrate better performance over the conventional sliding mode control. PMID:15098583
Combustion control of municipal incinerators by fuzzy neural network logic
Chang, N.B.; Chang, Y.H.
The successful operation of mass burn waterwall incinerators involves many uncertain factors. Not only the physical composition and chemical properties of the refuse but also the complexity of combustion mechanism would significantly influence the performance of waste treatment. Due to the rising concerns of dioxin/furan emissions from municipal incinerators, improved combustion control algorithms, such as fuzzy and its fusion control technologies, have gradually received attention in the scientific community. This paper describes a fuzzy and neural network control logic for the refuse combustion process in a mass burn waterwall incinerator. It is anticipated that this system can also be easily applied to several other types of municipal incinerators, such as modular, rotary kiln, RDF and fluidized bed incinerators, by slightly modified steps. Partial performance of this designed controller is tested by computer simulation using identified process model in this analysis. Process control could be sensitive especially for the control of toxic substance emissions, such as dioxin and furans.
An application of fuzzy logic to power generation control
Tarabishy, M.N.; Grudzinski, J.J.
The high demand for more energy at lower prices, coupled with tighter safety and environmental regulations made it necessary for utility companies to provide reliable power more efficiently, and for that purpose new control methods are being utilized to meet those challenges. Fuzzy Logic Control (FLC) technology produces controllers that are more robust at lower development cost and time. These qualities give FLC advantage over conventional control technologies particularly in dealing with increasingly complex nonlinear systems. In this paper the authors examine some of the main applications of FLC in power systems and demonstrate it`s usefulness in the control of a gas turbine.
The report gives results of a study (part 1) of fuzzy logic motor control (FLMC). The study included: 1) reviews of existing applications of fuzzy logic, of motor operation, and of motor control; 2) a description of motor control schemes that can utilize FLMC; 3) selection of a m...
Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 1
NASA Technical Reports Server (NTRS)
Lea, Robert N. (Editor); Villarreal, James (Editor)
Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Houston, Clear Lake. The workshop was held April 11 to 13 at the Johnson Space Flight Center. Technical topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making.
Application of fuzzy logic in multipassive acoustic tracking
NASA Astrophysics Data System (ADS)
Ng, Gee W.
In this paper, we introduce the application of fuzzy logic concepts to solve the time-delay problem in tracking moving target using passive acoustic sensors. Passive tracking which uses the direction of arrival or bearing of a target is a nontrivial task. The problem is made even more difficult to solve if the passive sensors measurement of bearing is based on acoustic signal only. This is because the acoustic signal introduce time-delay i.e. different senors spatially apart will receive the same target's acoustic signal at different time. The time-delay problem cannot be resolve easily partly because the amplitude of the acoustic signal strength cannot be modeled linearly; its behavior is nonlinear subjected to environmental conditions. To solve these problems we propose to apply the fuzzy logic concept, using information from sensors such as amplitude difference and time-stamp difference from different sensors. The defuzzified results provide one of the main factors for computing the correlation strength between different bearing tracks. The two tracks with the highest correlation strength are then used to determine the position of the target.
Automated maneuver planning using a fuzzy logic algorithm
NASA Technical Reports Server (NTRS)
Conway, D.; Sperling, R.; Folta, D.; Richon, K.; Defazio, R.
Spacecraft orbital control requires intensive interaction between the analyst and the system used to model the spacecraft trajectory. For orbits with right mission constraints and a large number of maneuvers, this interaction is difficult or expensive to accomplish in a timely manner. Some automation of maneuver planning can reduce these difficulties for maneuver-intensive missions. One approach to this automation is to use fuzzy logic in the control mechanism. Such a prototype system currently under development is discussed. The Tropical Rainfall Measurement Mission (TRMM) is one of several missions that could benefit from automated maneuver planning. TRMM is scheduled for launch in August 1997. The spacecraft is to be maintained in a 350-km circular orbit throughout the 3-year lifetime of the mission, with very small variations in this orbit allowed. Since solar maximum will occur as early as 1999, the solar activity during the TRMM mission will be increasing. The increasing solar activity will result in orbital maneuvers being performed as often as every other day. The results of automated maneuver planning for the TRMM mission will be presented to demonstrate the prototype of the fuzzy logic tool.
Estimating outcomes in newborn infants using fuzzy logic
Chaves, Luciano Eustáquio; Nascimento, Luiz Fernando C.
OBJECTIVE: To build a linguistic model using the properties of fuzzy logic to estimate the risk of death of neonates admitted to a Neonatal Intensive Care Unit. METHODS: Computational model using fuzzy logic. The input variables of the model were birth weight, gestational age, 5th-minute Apgar score and inspired fraction of oxygen in newborn infants admitted to a Neonatal Intensive Care Unit of Taubaté, Southeast Brazil. The output variable was the risk of death, estimated as a percentage. Three membership functions related to birth weight, gestational age and 5th-minute Apgar score were built, as well as two functions related to the inspired fraction of oxygen; the risk presented five membership functions. The model was developed using the Mandani inference by means of Matlab(r) software. The model values were compared with those provided by experts and their performance was estimated by ROC curve. RESULTS: 100 newborns were included, and eight of them died. The model estimated an average possibility of death of 49.7±29.3%, and the possibility of hospital discharge was 24±17.5%. These values are different when compared by Student's t-test (p<0.001). The correlation test revealed r=0.80 and the performance of the model was 81.9%. CONCLUSIONS: This predictive, non-invasive and low cost model showed a good accuracy and can be applied in neonatal care, given the easiness of its use. PMID:25119746
Fuzzy logic control for an automated guided vehicle
NASA Astrophysics Data System (ADS)
Cao, Ming; Hall, Ernest L.
This paper describes the use of fuzzy logic control for the high level control systems of a mobile robot. The advantages of the fuzzy logic system are that multiple types of input such as that from vision and sonar sensors as well as stored map information can be used to guide the robot. Sensor fusion can be accomplished between real time sensed information and stored information in a manner similar to a human decision maker. Vision guidance is accomplished with a CCD camera with a zoom lens. The data is collected through a commercial tracking device, communicating to the computer the X,Y coordinates of a lane marker. Testing of these systems yielded positive results by showing that at five miles per hour, the vehicle can follow a line and avoid obstacles. The obstacle detection uses information from Polaroid sonar detection system. The motor control system uses a programmable Galil motion control system. This design, in its modularity, creates a portable autonomous controller that could be used for any mobile vehicle with only minor adaptations.
Fuzzy Logic Based Autonomous Parallel Parking System with Kalman Filtering
NASA Astrophysics Data System (ADS)
Panomruttanarug, Benjamas; Higuchi, Kohji
This paper presents an emulation of fuzzy logic control schemes for an autonomous parallel parking system in a backward maneuver. There are four infrared sensors sending the distance data to a microcontroller for generating an obstacle-free parking path. Two of them mounted on the front and rear wheels on the parking side are used as the inputs to the fuzzy rules to calculate a proper steering angle while backing. The other two attached to the front and rear ends serve for avoiding collision with other cars along the parking space. At the end of parking processes, the vehicle will be in line with other parked cars and positioned in the middle of the free space. Fuzzy rules are designed based upon a wall following process. Performance of the infrared sensors is improved using Kalman filtering. The design method needs extra information from ultrasonic sensors. Starting from modeling the ultrasonic sensor in 1-D state space forms, one makes use of the infrared sensor as a measurement to update the predicted values. Experimental results demonstrate the effectiveness of sensor improvement.
Japanese advances in fuzzy systems research
NASA Astrophysics Data System (ADS)
Schwartz, Daniel G.
During this past summer (1991), I spent two months on an appointment as visiting researcher at Kansai University, Osaka, Japan, and five weeks at the Laboratory for International Fuzzy Engineering Research (LIFE), in Yokohama. Part of the expenses for the time in Osaka, and all the expenses for the visit at LIFE, were covered by ONR. While there I met with most of the key researchers in both fuzzy systems and case-based reasoning. This involved trips to numerous universities and research laboratories at Matsushita/Panasonic, Omron, and Hitachi Corporations. In addition, I spent three days at the Fuzzy Logic Systems Institute (FLSI), Iizuka, and I attended the annual meeting of the Japan Society for Fuzzy Theory and Research (SOFT-91) in Nagoya. The following report elaborates what I learned as a result of those activities.
Simple fuzzy logic estimation of flow forecast uncertainty
NASA Astrophysics Data System (ADS)
Danhelka, Jan
Fuzzy logic is recognized as useful tool to support for decision making under uncertainty. As such some methods for reservoir operation or real time flood management were developed. Maskey (2004) describes method of model uncertainty assessment based on qualitative expert judgement and its representation in fuzzy space. It is based on categorical judging of the quality and importance of selected model parameters (processes). The method was modified in order to reflect varying uncertainty of single model realization (forecast) with respect to inputting precipitation forecast (QPF). Two model uncertainty parameters were distinguish: 1) QPF, 2) model uncertainty due to concept and parameters. The approach was tested and applied for Černá river basin (127 km2) in southern Bohemia for the period from January 2008. Aqualog forecasting system (SAC-SMA implemented) is used for real time forecasting within the basin. It provides deterministic QPF based (NWP ALADIN) forecast with 48 h lead time. The aim of the study was to estimate the uncertainty of the forecast using simple fuzzy procedure. QPF uncertainty dominates the total uncertainty of hydrological forecast in condition of the Czech Republic. Therefore an evaluation of QPF performance was done for the basin. Based on detected quantiles of relative difference the fuzzy expression of QPF exceedance probability was done to represent the quality of QPF parameter. We further assumed that the importance of QPF parameter is proportional to its quality. Model uncertainty was qualitatively estimated to be moderate both in quality and importance. Than the fuzzy sum of both parameters was computed. The output is than fitted to deterministic flow forecast using the highest forecasted flow and its known reference in fuzzy space (determined according to QPF performance evaluation). The case study provided promising results in the meaning of Brier skill score (0.24) as well as in comparison of forecasted to expected distribution
Qualitative information modeling: The role of fuzzy logic in project economic evaluations
Warnken, P.G.
Conventional models rely on a precise mathematical formalism to express the quantitative essentials of the system being modeled. In contrast, decisionmakers in the real world employ cognitive skills to process information and arrive at decisions based on judgement and experience. Bridging the gap between the two analytic approaches -- that is, formulating intelligent models -- has met with very limited success using traditional computational methods. The difficulty stems from two problems. First, imprecision, which is the distinguishing feature of qualitative factors, is an information attribute that is not easily computable using the rules of traditional set theory and Boolean (bivalent) logic. Second, cognitive information processing is cumbersome using the numerical rule-based approaches common in today`s expert systems. Fuzzy models overcome these problems by employing new mathematical rules for expressing and processing knowledge. These rules are based on fuzzy logic. Fuzzy logic is the formal symbolic language used to represent linguistic terms and verbal rules for computational and modeling purposes. This language provides model builders with the means to incorporate subjective judgements, imprecise information, and human reasoning capabilities as part of a model`s framework. This paper outlines the concepts needed to understand fuzzy modeling systems. The key concepts discussed include fuzzy sets, fuzzy logical operators, linguistic variables, and verbal rules. A simple fuzzy economic rating model for project investments is presented to demonstrate the fuzzy modeling technique. Finally, the paper discusses the role of fuzzy logic in the economic modeling process.
Multi-objective decision-making under uncertainty: Fuzzy logic methods
NASA Technical Reports Server (NTRS)
Hardy, Terry L.
Fuzzy logic allows for quantitative representation of vague or fuzzy objectives, and therefore is well-suited for multi-objective decision-making. This paper presents methods employing fuzzy logic concepts to assist in the decision-making process. In addition, this paper describes software developed at NASA Lewis Research Center for assisting in the decision-making process. Two diverse examples are used to illustrate the use of fuzzy logic in choosing an alternative among many options and objectives. One example is the selection of a lunar lander ascent propulsion system, and the other example is the selection of an aeration system for improving the water quality of the Cuyahoga River in Cleveland, Ohio. The fuzzy logic techniques provided here are powerful tools which complement existing approaches, and therefore should be considered in future decision-making activities.
A Modern Syllogistic Method in Intuitionistic Fuzzy Logic with Realistic Tautology
Rushdi, Ali Muhammad; Zarouan, Mohamed; Alshehri, Taleb Mansour; Rushdi, Muhammad Ali
The Modern Syllogistic Method (MSM) of propositional logic ferrets out from a set of premises all that can be concluded from it in the most compact form. The MSM combines the premises into a single function equated to 1 and then produces the complete product of this function. Two fuzzy versions of MSM are developed in Ordinary Fuzzy Logic (OFL) and in Intuitionistic Fuzzy Logic (IFL) with these logics augmented by the concept of Realistic Fuzzy Tautology (RFT) which is a variable whose truth exceeds 0.5. The paper formally proves each of the steps needed in the conversion of the ordinary MSM into a fuzzy one. The proofs rely mainly on the successful replacement of logic 1 (or ordinary tautology) by an RFT. An improved version of Blake-Tison algorithm for generating the complete product of a logical function is also presented and shown to be applicable to both crisp and fuzzy versions of the MSM. The fuzzy MSM methodology is illustrated by three specific examples, which delineate differences with the crisp MSM, address the question of validity values of consequences, tackle the problem of inconsistency when it arises, and demonstrate the utility of the concept of Realistic Fuzzy Tautology. PMID:26380357
WARP: Weight Associative Rule Processor. A dedicated VLSI fuzzy logic megacell
NASA Technical Reports Server (NTRS)
Pagni, A.; Poluzzi, R.; Rizzotto, G. G.
During the last five years Fuzzy Logic has gained enormous popularity in the academic and industrial worlds. The success of this new methodology has led the microelectronics industry to create a new class of machines, called Fuzzy Machines, to overcome the limitations of traditional computing systems when utilized as Fuzzy Systems. This paper gives an overview of the methods by which Fuzzy Logic data structures are represented in the machines (each with its own advantages and inefficiencies). Next, the paper introduces WARP (Weight Associative Rule Processor) which is a dedicated VLSI megacell allowing the realization of a fuzzy controller suitable for a wide range of applications. WARP represents an innovative approach to VLSI Fuzzy controllers by utilizing different types of data structures for characterizing the membership functions during the various stages of the Fuzzy processing. WARP dedicated architecture has been designed in order to achieve high performance by exploiting the computational advantages offered by the different data representations.
Application of Adaptive Neuro Fuzzy Inference System (ANFIS) In Implementing of New CMOS Fuzzy Logic Controller (FLC) Chip
NASA Astrophysics Data System (ADS)
Aminifar, S.; Yosefi, Gh.
In this paper, we present away of using Anfis architecture to implement a new fuzzy logic controller chip. Anfis which tunes the fuzzy inference system with a backpropagation algorithm based on collection of input-output data makes fuzzy system to learn. This training is given from a standard response of the system and membership functions are suitably modified. For adaptive Anfis based fuzzy controller and its circuit design, we propose new circuits for implementing each controller block, and illustrate the test results and control surface of Anfis controller along with CMOS fuzzy logic controller using Matlab and Hspice software respectively. For implementing controller according to the Anfis training, we proposed new and improved integrated circuits which consist of Fuzzifier, Min operator and Multiplier/Divider. The control surfaces of controller are obtained by using Anfis training and simulation results of integrated circuits in less than 0.075 mm2 area in 0.35 μm CMOS standard technology.
Wastewater neutralization control based on fuzzy logic: Experimental results
Adroer, M.; Alsina, A.; Aumatell, J.; Poch, M.
Many industrial wastes contain acidic or alkaline materials that require neutralization of previous discharge into receiving waters or to chemical and biological treatment plants. The control of the wastewater neutralization process is subjected to several difficulties, such as the highly nonlinear titration curve (with special sensitivity around neutrality), the unknown water composition, the variable buffering capacity of the system, and the changes in input loading. To deal with these problems, this study proposes a fixed fuzzy logic controller (FLC) structure coupled with a tuning factor. The versatility and robustness of this controller has been proved when faced with solutions of variable buffering capacity, with acids that cover a wide pK range and with switches between acids throughout the course of a test. Laboratory experiments and simulation runs using the proposed controller were successful in a wide operational range.
Forest fire autonomous decision system based on fuzzy logic
NASA Astrophysics Data System (ADS)
Lei, Z.; Lu, Jianhua
The proposed system integrates GPS / pseudolite / IMU and thermal camera in order to autonomously process the graphs by identification, extraction, tracking of forest fire or hot spots. The airborne detection platform, the graph-based algorithms and the signal processing frame are analyzed detailed; especially the rules of the decision function are expressed in terms of fuzzy logic, which is an appropriate method to express imprecise knowledge. The membership function and weights of the rules are fixed through a supervised learning process. The perception system in this paper is based on a network of sensorial stations and central stations. The sensorial stations collect data including infrared and visual images and meteorological information. The central stations exchange data to perform distributed analysis. The experiment results show that working procedure of detection system is reasonable and can accurately output the detection alarm and the computation of infrared oscillations.
Forest fire autonomous decision system based on fuzzy logic
NASA Astrophysics Data System (ADS)
Lei, Z.; Lu, Jianhua
The proposed system integrates GPS / pseudolite / IMU and thermal camera in order to autonomously process the graphs by identification, extraction, tracking of forest fire or hot spots. The airborne detection platform, the graph-based algorithms and the signal processing frame are analyzed detailed; especially the rules of the decision function are expressed in terms of fuzzy logic, which is an appropriate method to express imprecise knowledge. The membership function and weights of the rules are fixed through a supervised learning process. The perception system in this paper is based on a network of sensorial stations and central stations. The sensorial stations collect data including infrared and visual images and meteorological information. The central stations exchange data to perform distributed analysis. The experiment results show that working procedure of detection system is reasonable and can accurately output the detection alarm and the computation of infrared oscillations.
Controlling of grid connected photovoltaic lighting system with fuzzy logic
Saglam, Safak; Ekren, Nazmi; Erdal, Hasan
In this study, DC electrical energy produced by photovoltaic panels is converted to AC electrical energy and an indoor area is illuminated using this energy. System is controlled by fuzzy logic algorithm controller designed with 16 rules. Energy is supplied from accumulator which is charged by photovoltaic panels if its energy would be sufficient otherwise it is supplied from grid. During the 1-week usage period at the semester time, 1.968 kWh energy is used from grid but designed system used 0.542 kWh energy from photovoltaic panels at the experiments. Energy saving is determined by calculations and measurements for one education year period (9 months) 70.848 kWh. (author)
Fuzzy Logic Based Rotor Health Index of Induction Motor
NASA Astrophysics Data System (ADS)
Misra, Rajul; Pahuja, G. L.
This paper presents an experimental study on detection and diagnosis of broken rotor bars in Squirrel Cage Induction Motor (SQIM). The proposed scheme is based on Motor Current Signature Analysis (MCSA) which uses amplitude difference of supply frequency to upper and lower side bands. Initially traditional MCSA has been used for rotor fault detection. It provides rotor health index on full load conditions. However in real practice if a fault occurs motor can not run at full load. To overcome the issue of reduced load condition a Fuzzy Logic based MCSA has been designed, implemented, tested and compared with traditional MCSA. A simulation result shows that proposed scheme is not only capable of detecting the severity of rotor fault but also provides remarkable performance at reduced load conditions.
Adaptive process control using fuzzy logic and genetic algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Adaptive Process Control with Fuzzy Logic and Genetic Algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Fuzzy logic for identifying pigments studied by Raman spectroscopy.
Ramos, Pablo Manuel; Ferré, Joan; Ruisánchez, Itziar; Andrikopoulos, Konstantinos S
Fuzzy logic and linguistic variables are used for the automatic interpretation of Raman spectra obtained from pigments found in cultural heritage art objects. Featured bands are extracted from a Raman spectrum of a reference pigment and the methodology for constructing the library is illustrated. An unknown spectrum is then interpreted automatically and a process for identifying the corresponding pigment is described. A reference library consisting of 32 pigments was built and the effectiveness of the algorithm was tested by the Raman spectroscopic analysis of 10 pigments that are known to have been extensively used in Byzantine hagiography. Binary mixtures of these pigments were also tested. The algorithm's level of identification was good even though extra peaks, noise, and background signals were encountered in the spectra. PMID:15282052
Automated interpretation of LIBS spectra using a fuzzy logic inference engine.
Hatch, Jeremy J; McJunkin, Timothy R; Hanson, Cynthia; Scott, Jill R
Automated interpretation of laser-induced breakdown spectroscopy (LIBS) data is necessary due to the plethora of spectra that can be acquired in a relatively short time. However, traditional chemometric and artificial neural network methods that have been employed are not always transparent to a skilled user. A fuzzy logic approach to data interpretation has now been adapted to LIBS spectral interpretation. Fuzzy logic inference rules were developed using methodology that includes data mining methods and operator expertise to differentiate between various copper-containing and stainless steel alloys as well as unknowns. Results using the fuzzy logic inference engine indicate a high degree of confidence in spectral assignment. PMID:22410914
A comprehensive approach using fuzzy logic to select fracture fluid systems
Xiong, H.; Davidson, B.; Holditch, S.A.; Saunders, B.
This system, which consists of several fuzzy logic evaluators, can also be applied to similar problems associated with drilling, completing and working over wells. With formation information, the fuzzy logic system first determines base fluid, viscosifying method and energization method before choosing the 3--5 best combinations of possible fluids. The system then determines polymer type and loading, crosslinker, gas type if necessary, and other additives for the fluid systems. Also using fuzzy logic, this system checks the compatibility of the fluid and additives with formation fluids and composition.
A fuzzy logic excitation system for stability enhancement of power systems with multi-mode oscillations
Hiyama, Takashi; Miyazaki, Koushi; Satoh, Hironori
A fuzzy logic excitation system has been proposed to enhance the overall stability of power systems. The proposed excitation system has two control loops. One is the voltage control loop which achieves the automatic voltage regulator (AVR) function, and the other is the damping control loop which gives the PSS function. Simple fuzzy logic control rules are applied to both loops. The input signal to the voltage control loop is the terminal voltage, and the input signal to the damping control loop is the real power output. Simulation studies show the advantages of the fuzzy logic excitation system.
Virtual reality simulation of fuzzy-logic control during underwater dynamic positioning
NASA Astrophysics Data System (ADS)
Thekkedan, Midhin Das; Chin, Cheng Siong; Woo, Wai Lok
In this paper, graphical-user-interface (GUI) software for simulation and fuzzy-logic control of a remotely operated vehicle (ROV) using MATLAB™ GUI Designing Environment is proposed. The proposed ROV's GUI platform allows the controller such as fuzzy-logic control systems design to be compared with other controllers such as proportional-integral-derivative (PID) and sliding-mode controller (SMC) systematically and interactively. External disturbance such as sea current can be added to improve the modelling in actual underwater environment. The simulated results showed the position responses of the fuzzy-logic control exhibit reasonable performance under the sea current disturbance.
Power control of SAFE reactor using fuzzy logic
NASA Astrophysics Data System (ADS)
Irvine, Claude
Controlling the 100 kW SAFE (Safe Affordable Fission Engine) reactor consists of design and implementation of a fuzzy logic process control system to regulate dynamic variables related to nuclear system power. The first phase of development concentrates primarily on system power startup and regulation, maintaining core temperature equilibrium, and power profile matching. This paper discusses the experimental work performed in those areas. Nuclear core power from the fuel elements is simulated using resistive heating elements while heat rejection is processed by a series of heat pipes. Both axial and radial nuclear power distributions are determined from neuronic modeling codes. The axial temperature profile of the simulated core is matched to the nuclear power profile by varying the resistance of the heating elements. The SAFE model establishes radial temperature profile equivalence by establishing 32 control zones as the nodal coordinates. Control features also allow for slow warm up, since complete shutoff can occur in the heat pipes if heat-source temperatures drop/rise below a certain minimum value, depending on the specific fluid and gas combination in the heat pipe. The entire system is expected to be self-adaptive, i.e., capable of responding to long-range changes in the space environment. Particular attention in the development of the fuzzy logic algorithm shall ensure that the system process remains at set point, virtually eliminating overshoot on start-up and during in-process disturbances. The controller design will withstand harsh environments and applications where it might come in contact with water, corrosive chemicals, radiation fields, etc. .
Using fuzzy logic to enhance stereo matching in multiresolution images.
Medeiros, Marcos D; Gonçalves, Luiz Marcos G; Frery, Alejandro C
Stereo matching is an open problem in computer vision, for which local features are extracted to identify corresponding points in pairs of images. The results are heavily dependent on the initial steps. We apply image decomposition in multiresolution levels, for reducing the search space, computational time, and errors. We propose a solution to the problem of how deep (coarse) should the stereo measures start, trading between error minimization and time consumption, by starting stereo calculation at varying resolution levels, for each pixel, according to fuzzy decisions. Our heuristic enhances the overall execution time since it only employs deeper resolution levels when strictly necessary. It also reduces errors because it measures similarity between windows with enough details. We also compare our algorithm with a very fast multi-resolution approach, and one based on fuzzy logic. Our algorithm performs faster and/or better than all those approaches, becoming, thus, a good candidate for robotic vision applications. We also discuss the system architecture that efficiently implements our solution. PMID:22205859
Using Fuzzy Logic to Enhance Stereo Matching in Multiresolution Images
Medeiros, Marcos D.; Gonçalves, Luiz Marcos G.; Frery, Alejandro C.
Stereo matching is an open problem in Computer Vision, for which local features are extracted to identify corresponding points in pairs of images. The results are heavily dependent on the initial steps. We apply image decomposition in multiresolution levels, for reducing the search space, computational time, and errors. We propose a solution to the problem of how deep (coarse) should the stereo measures start, trading between error minimization and time consumption, by starting stereo calculation at varying resolution levels, for each pixel, according to fuzzy decisions. Our heuristic enhances the overall execution time since it only employs deeper resolution levels when strictly necessary. It also reduces errors because it measures similarity between windows with enough details. We also compare our algorithm with a very fast multi-resolution approach, and one based on fuzzy logic. Our algorithm performs faster and/or better than all those approaches, becoming, thus, a good candidate for robotic vision applications. We also discuss the system architecture that efficiently implements our solution. PMID:22205859
A fuzzy logic controller for an autonomous mobile robot
NASA Technical Reports Server (NTRS)
Yen, John; Pfluger, Nathan
The ability of a mobile robot system to plan and move intelligently in a dynamic system is needed if robots are to be useful in areas other than controlled environments. An example of a use for this system is to control an autonomous mobile robot in a space station, or other isolated area where it is hard or impossible for human life to exist for long periods of time (e.g., Mars). The system would allow the robot to be programmed to carry out the duties normally accomplished by a human being. Some of the duties that could be accomplished include operating instruments, transporting objects, and maintenance of the environment. The main focus of our early work has been on developing a fuzzy controller that takes a path and adapts it to a given environment. The robot only uses information gathered from the sensors, but retains the ability to avoid dynamically placed obstacles near and along the path. Our fuzzy logic controller is based on the following algorithm: (1) determine the desired direction of travel; (2) determine the allowed direction of travel; and (3) combine the desired and allowed directions in order to determine a direciton that is both desired and allowed. The desired direction of travel is determined by projecting ahead to a point along the path that is closer to the goal. This gives a local direction of travel for the robot and helps to avoid obstacles.
A Fuzzy Logic Framework for Integrating Multiple Learned Models
Bobi Kai Den Hartog
The Artificial Intelligence field of Integrating Multiple Learned Models (IMLM) explores ways to combine results from sets of trained programs. Aroclor Interpretation is an ill-conditioned problem in which trained programs must operate in scenarios outside their training ranges because it is intractable to train them completely. Consequently, they fail in ways related to the scenarios. We developed a general-purpose IMLM solution, the Combiner, and applied it to Aroclor Interpretation. The Combiner's first step, Scenario Identification (M), learns rules from very sparse, synthetic training data consisting of results from a suite of trained programs called Methods. S1 produces fuzzy belief weights for each scenario by approximately matching the rules. The Combiner's second step, Aroclor Presence Detection (AP), classifies each of three Aroclors as present or absent in a sample. The third step, Aroclor Quantification (AQ), produces quantitative values for the concentration of each Aroclor in a sample. AP and AQ use automatically learned empirical biases for each of the Methods in each scenario. Through fuzzy logic, AP and AQ combine scenario weights, automatically learned biases for each of the Methods in each scenario, and Methods' results to determine results for a sample.
Autonomous Control of a Quadrotor UAV Using Fuzzy Logic
NASA Astrophysics Data System (ADS)
Sureshkumar, Vijaykumar
UAVs are being increasingly used today than ever before in both military and civil applications. They are heavily preferred in "dull, dirty or dangerous" mission scenarios. Increasingly, UAVs of all kinds are being used in policing, fire-fighting, inspection of structures, pipelines etc. Recently, the FAA gave its permission for UAVs to be used on film sets for motion capture and high definition video recording. The rapid development in MEMS and actuator technology has made possible a plethora of UAVs that are suited for commercial applications in an increasingly cost effective manner. An emerging popular rotary wing UAV platform is the Quadrotor A Quadrotor is a helicopter with four rotors, that make it more stable; but more complex to model and control. Characteristics that provide a clear advantage over other fixed wing UAVs are VTOL and hovering capabilities as well as a greater maneuverability. It is also simple in construction and design compared to a scaled single rotorcraft. Flying such UAVs using a traditional radio Transmitter-Receiver setup can be a daunting task especially in high stress situations. In order to make such platforms widely applicable, a certain level of autonomy is imperative to the future of such UAVs. This thesis paper presents a methodology for the autonomous control of a Quadrotor UAV using Fuzzy Logic. Fuzzy logic control has been chosen over conventional control methods as it can deal effectively with highly nonlinear systems, allows for imprecise data and is extremely modular. Modularity and adaptability are the key cornerstones of FLC. The objective of this thesis is to present the steps of designing, building and simulating an intelligent flight control module for a Quadrotor UAV. In the course of this research effort, a Quadrotor UAV is indigenously developed utilizing the resources of an online open source project called Aeroquad. System design is comprehensively dealt with. A math model for the Quadrotor is developed and a
Preventive Maintenance Prioritization by Fuzzy Logic for Seamless Hydro Power Generation
NASA Astrophysics Data System (ADS)
Roy, P. K.; Adhikary, P.; Mazumdar, A.
Preventive maintenance prioritization is one of the most important criteria for the electricity generation planners to minimize the down time and production costs. Break down of equipments increases costs and plant down time results in loss of business. This work focuses on prioritizing the preventive maintenance for seamless hydro power generation considering (24 × 7) client's power demand using fuzzy logic. The main task involves prioritizing the maintenance work considering constraints of varied power demand and hydro turbine plant breakdown. Fuzzy logic is used to optimize the preventive maintenance prioritization under the main constraints. Manual fuzzy arithmetic is used to develop the model and MATLAB Fuzzy Inference System editor used to validate the same. This novel fuzzy logic approach of preventive maintenance prioritizing for hydro power generation is absent in renewable power generation and industrial engineering literatures due to its assessment complexity.
A fuzzy logic sliding mode controlled electronic differential for a direct wheel drive EV
NASA Astrophysics Data System (ADS)
Ozkop, Emre; Altas, Ismail H.; Okumus, H. Ibrahim; Sharaf, Adel M.
In this study, a direct wheel drive electric vehicle based on an electronic differential system with a fuzzy logic sliding mode controller (FLSMC) is studied. The conventional sliding surface is modified using a fuzzy rule base to obtain fuzzy dynamic sliding surfaces by changing its slopes using the global error and its derivative in a fuzzy logic inference system. The controller is compared with proportional-integral-derivative (PID) and sliding mode controllers (SMCs), which are usually preferred to be used in industry. The proposed controller provides robustness and flexibility to direct wheel drive electric vehicles. The fuzzy logic sliding mode controller, electronic differential system and the overall electrical vehicle mechanism are modelled and digitally simulated by using the Matlab software. Simulation results show that the system with FLSMC has better efficiency and performance compared to those of PID and SMCs.
Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.
Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko
In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. PMID:26774211
Fuzzy logic feedback control for fed-batch enzymatic hydrolysis of lignocellulosic biomass.
Tai, Chao; Voltan, Diego S; Keshwani, Deepak R; Meyer, George E; Kuhar, Pankaj S
A fuzzy logic feedback control system was developed for process monitoring and feeding control in fed-batch enzymatic hydrolysis of a lignocellulosic biomass, dilute acid-pretreated corn stover. Digested glucose from hydrolysis reaction was assigned as input while doser feeding time and speed of pretreated biomass were responses from fuzzy logic control system. Membership functions for these three variables and rule-base were created based on batch hydrolysis data. The system response was first tested in LabVIEW environment then the performance was evaluated through real-time hydrolysis reaction. The feeding operations were determined timely by fuzzy logic control system and efficient responses were shown to plateau phases during hydrolysis. Feeding of proper amount of cellulose and maintaining solids content was well balanced. Fuzzy logic proved to be a robust and effective online feeding control tool for fed-batch enzymatic hydrolysis. PMID:26915095
Fuzzy logic and image processing techniques for the interpretation of seismic data
NASA Astrophysics Data System (ADS)
Orozco-del-Castillo, M. G.; Ortiz-Alemán, C.; Urrutia-Fucugauchi, J.; Rodríguez-Castellanos, A.
Since interpretation of seismic data is usually a tedious and repetitive task, the ability to do so automatically or semi-automatically has become an important objective of recent research. We believe that the vagueness and uncertainty in the interpretation process makes fuzzy logic an appropriate tool to deal with seismic data. In this work we developed a semi-automated fuzzy inference system to detect the internal architecture of a mass transport complex (MTC) in seismic images. We propose that the observed characteristics of a MTC can be expressed as fuzzy if-then rules consisting of linguistic values associated with fuzzy membership functions. The constructions of the fuzzy inference system and various image processing techniques are presented. We conclude that this is a well-suited problem for fuzzy logic since the application of the proposed methodology yields a semi-automatically interpreted MTC which closely resembles the MTC from expert manual interpretation.
Use of fuzzy logic in lignite inventory estimation
Tutmez, B.; Dag, A.
Seam thickness is one of the most important parameters for reserve estimation of a lignite deposit. This paper addresses a case study on fuzzy estimation of lignite seam thickness from spatial coordinates. From the relationships between input (Cartesian coordinates) and output (thickness) parameters, fuzzy clustering and a fuzzy rule-based inference system were designed. Data-driven fuzzy model parameters were derived from numerical values directly. In addition, estimations of the fuzzy model were compared with kriging estimations. It was concluded that the performance ofthe fuzzy model was more satisfactory. The results indicated that the fuzzy modeling approach is very reliable for the estimation of lignite reserves.
Simulation of the Predictive Control Algorithm for Container Crane Operation using Matlab Fuzzy Logic Tool Box
NASA Technical Reports Server (NTRS)
Richardson, Albert O.
This research has investigated the use of fuzzy logic, via the Matlab Fuzzy Logic Tool Box, to design optimized controller systems. The engineering system for which the controller was designed and simulate was the container crane. The fuzzy logic algorithm that was investigated was the 'predictive control' algorithm. The plant dynamics of the container crane is representative of many important systems including robotic arm movements. The container crane that was investigated had a trolley motor and hoist motor. Total distance to be traveled by the trolley was 15 meters. The obstruction height was 5 meters. Crane height was 17.8 meters. Trolley mass was 7500 kilograms. Load mass was 6450 kilograms. Maximum trolley and rope velocities were 1.25 meters per sec. and 0.3 meters per sec., respectively. The fuzzy logic approach allowed the inclusion, in the controller model, of performance indices that are more effectively defined in linguistic terms. These include 'safety' and 'cargo swaying'. Two fuzzy inference systems were implemented using the Matlab simulation package, namely the Mamdani system (which relates fuzzy input variables to fuzzy output variables), and the Sugeno system (which relates fuzzy input variables to crisp output variable). It is found that the Sugeno FIS is better suited to including aspects of those plant dynamics whose mathematical relationships can be determined.
Modelling of Reservoir Operations using Fuzzy Logic and ANNs
NASA Astrophysics Data System (ADS)
Van De Giesen, N.; Coerver, B.; Rutten, M.
Today, almost 40.000 large reservoirs, containing approximately 6.000 km3 of water and inundating an area of almost 400.000 km2, can be found on earth. Since these reservoirs have a storage capacity of almost one-sixth of the global annual river discharge they have a large impact on the timing, volume and peaks of river discharges. Global Hydrological Models (GHM) are thus significantly influenced by these anthropogenic changes in river flows. We developed a parametrically parsimonious method to extract operational rules based on historical reservoir storage and inflow time-series. Managing a reservoir is an imprecise and vague undertaking. Operators always face uncertainties about inflows, evaporation, seepage losses and various water demands to be met. They often base their decisions on experience and on available information, like reservoir storage and the previous periods inflow. We modeled this decision-making process through a combination of fuzzy logic and artificial neural networks in an Adaptive-Network-based Fuzzy Inference System (ANFIS). In a sensitivity analysis, we compared results for reservoirs in Vietnam, Central Asia and the USA. ANFIS can indeed capture reservoirs operations adequately when fed with a historical monthly time-series of inflows and storage. It was shown that using ANFIS, operational rules of existing reservoirs can be derived without much prior knowledge about the reservoirs. Their validity was tested by comparing actual and simulated releases with each other. For the eleven reservoirs modelled, the normalised outflow, <0,1>, was predicted with a MSE of 0.002 to 0.044. The rules can be incorporated into GHMs. After a network for a specific reservoir has been trained, the inflow calculated by the hydrological model can be combined with the release and initial storage to calculate the storage for the next time-step using a mass balance. Subsequently, the release can be predicted one time-step ahead using the inflow and storage.
Fuzzy logic controller versus classical logic controller for residential hybrid solar-wind-storage energy system
NASA Astrophysics Data System (ADS)
Derrouazin, A.; Aillerie, M.; Mekkakia-Maaza, N.; Charles, J. P.
Several researches for management of diverse hybrid energy systems and many techniques have been proposed for robustness, savings and environmental purpose. In this work we aim to make a comparative study between two supervision and control techniques: fuzzy and classic logics to manage the hybrid energy system applied for typical housing fed by solar and wind power, with rack of batteries for storage. The system is assisted by the electric grid during energy drop moments. A hydrogen production device is integrated into the system to retrieve surplus energy production from renewable sources for the household purposes, intending the maximum exploitation of these sources over years. The models have been achieved and generated signals for electronic switches command of proposed both techniques are presented and discussed in this paper.
Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 1
NASA Technical Reports Server (NTRS)
Culbert, Christopher J. (Editor)
Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake. The workshop was held June 1-3, 1992 at the Lyndon B. Johnson Space Center in Houston, Texas. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control, and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making.
Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2
NASA Technical Reports Server (NTRS)
Culbert, Christopher J. (Editor)
Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making.
Development of a fuzzy logic controller for dc/dc converters: Design, computer simulation, and experimental evaluation
So, W.C.; Tse, C.K.; Lee, Y.S.
The design of a fuzzy logic controller for dc/dc converters is described in this paper. A brief review of fuzzy logic and its application to control is first given. Then, the derivation of a fuzzy control algorithm for regulating dc/dc converters is described in detail. The proposed fuzzy control is evaluated by computer simulations as well as experimental measurements of the closed-loop performance of simple dc/dc converters in respect of load regulation and line regulation.
Fuzzy-logic-based active vibration control of beams using piezoelectric patches
NASA Astrophysics Data System (ADS)
Sharma, Manu; Singh, S. P.; Sachdeva, B. L.
The present work presents a fuzzy logic based controller with a compact rule base, for active vibration control of beams. The controller was implemented experimentally on a test beam and the results were found satisfactory. The test system consists of a cantilevered beam with two piezoelectric patches mounted near its root in collocated fashion. This piezo-beam system was modelled using Finite Element Method. To derive the equations of motion, Hamilton's principle was used. Electro-mechanical interaction of the piezoelectric patch with the beam was modelled using linear constitutive equations for piezoceramics, which relate strain and electric displacement to stress and electric field. The fuzzy logic controller is based on modal velocity of the beam. The basis for generating the fuzzy logic rule base of this controller is obtained from negative velocity feedback control. Modal velocity of the beam acts as an input to the fuzzy controller and actuation force is the output from the inference engine. Linear decay of vibratory amplitude is observed in case of fuzzy logic controller as opposed to logarithmic decay in case of negative velocity feedback control Present controller has just three rules. This is an important achievement because bulky fuzzy logic controllers for active vibration control require fast processors for real time implementation (Kwak and Sciulli and Mayhan and Washington).
Flexible automated parameterization of hydrologic models using fuzzy logic
NASA Astrophysics Data System (ADS)
Samanta, Sudeep; Mackay, D. Scott
Recent developments in model calibration suggest that information obtained from calibration is inherently uncertain in nature. Therefore identification of optimum parameter values is often highly nonspecific. A calibration framework using fuzzy logic is presented to deal with such uncertain information. An application of this technique to calibrate the streamflow of a hydrologic submodel embedded within an ecosystem simulation model demonstrates that objective estimates of parameter values and the range of model output associated with a failure to identify a unique solution can be obtained with suitable choices of objective functions. An iterative refinement in parameter estimates through a process of elimination was possible by incorporating multiple objective functions in calibration, thereby reducing the range of parameter values that capture the streamflow response. It is shown that objective function tradeoffs can lead to suboptimal solutions using the process of elimination without an automated procedure for reevaluation. Owing to its computational simplicity and flexibility this framework could be extended into a nonmonotonic system for automated parameter estimation.
Mobile Health in Maternal and Newborn Care: Fuzzy Logic
Premji, Shahirose
Whether mHealth improves maternal and newborn health outcomes remains uncertain as the response is perhaps not true or false but lies somewhere in between when considering unintended harmful consequences. Fuzzy logic, a mathematical approach to computing, extends the traditional binary "true or false" (one or zero) to exemplify this notion of partial truths that lies between completely true and false. The commentary explores health, socio-ecological and environmental consequences–positive, neutral or negative. Of particular significance is the negative influence of mHealth on maternal care-behaviors, which can increase stress reactivity and vulnerability to stress-induced illness across the lifespan of the child and establish pathways for intergenerational transmission of behaviors. A mHealth "fingerprinting" approach is essential to monitor psychosocial, economic, cultural, environmental and physical impact of mHealth intervention and make evidence-informed decision(s) about use of mHealth in maternal and newborn care. PMID:25003177
Automated mango fruit assessment using fuzzy logic approach
NASA Astrophysics Data System (ADS)
Hasan, Suzanawati Abu; Kin, Teoh Yeong; Sauddin@Sa'duddin, Suraiya; Aziz, Azlan Abdul; Othman, Mahmod; Mansor, Ab Razak; Parnabas, Vincent
In term of value and volume of production, mango is the third most important fruit product next to pineapple and banana. Accurate size assessment of mango fruits during harvesting is vital to ensure that they are classified to the grade accordingly. However, the current practice in mango industry is grading the mango fruit manually using human graders. This method is inconsistent, inefficient and labor intensive. In this project, a new method of automated mango size and grade assessment is developed using RGB fiber optic sensor and fuzzy logic approach. The calculation of maximum, minimum and mean values based on RGB fiber optic sensor and the decision making development using minimum entropy formulation to analyse the data and make the classification for the mango fruit. This proposed method is capable to differentiate three different grades of mango fruit automatically with 77.78% of overall accuracy compared to human graders sorting. This method was found to be helpful for the application in the current agricultural industry.
A new adaptive configuration of PID type fuzzy logic controller.
Fereidouni, Alireza; Masoum, Mohammad A S; Moghbel, Moayed
In this paper, an adaptive configuration for PID type fuzzy logic controller (FLC) is proposed to improve the performances of both conventional PID (C-PID) controller and conventional PID type FLC (C-PID-FLC). The proposed configuration is called adaptive because its output scaling factors (SFs) are dynamically tuned while the controller is functioning. The initial values of SFs are calculated based on its well-tuned counterpart while the proceeding values are generated using a proposed stochastic hybrid bacterial foraging particle swarm optimization (h-BF-PSO) algorithm. The performance of the proposed configuration is evaluated through extensive simulations for different operating conditions (changes in reference, load disturbance and noise signals). The results reveal that the proposed scheme performs significantly better over the C-PID controller and the C-PID-FLC in terms of several performance indices (integral absolute error (IAE), integral-of-time-multiplied absolute error (ITAE) and integral-of-time-multiplied squared error (ITSE)), overshoot and settling time for plants with and without dead time. PMID:25530256
Rocket engine system reliability analyses using probabilistic and fuzzy logic techniques
NASA Technical Reports Server (NTRS)
Hardy, Terry L.; Rapp, Douglas C.
The reliability of rocket engine systems was analyzed by using probabilistic and fuzzy logic techniques. Fault trees were developed for integrated modular engine (IME) and discrete engine systems, and then were used with the two techniques to quantify reliability. The IRRAS (Integrated Reliability and Risk Analysis System) computer code, developed for the U.S. Nuclear Regulatory Commission, was used for the probabilistic analyses, and FUZZYFTA (Fuzzy Fault Tree Analysis), a code developed at NASA Lewis Research Center, was used for the fuzzy logic analyses. Although both techniques provided estimates of the reliability of the IME and discrete systems, probabilistic techniques emphasized uncertainty resulting from randomness in the system whereas fuzzy logic techniques emphasized uncertainty resulting from vagueness in the system. Because uncertainty can have both random and vague components, both techniques were found to be useful tools in the analysis of rocket engine system reliability.
Fuzzy logic controller for the electric motor driving the astronomical telescope
NASA Astrophysics Data System (ADS)
Soliman, Hussein F.; Attia, Abdel-Fattah A.; Badr, Mohammed A.; Osman, Anas M.; Gamaleldin, Abdul A.
The paper presents an application of fuzzy logic controller to regulate the DC motor driver system of astronomical telescope. The mathematical model of such a telescope is highly nonlinear coupled equations. However, the accuracy requirement in telescope system exceed those of other industrial plants. Fuzzy logic controller provides means to deal with nonlinear functions. A fuzzy logic controller (FLC) was designed to enhance the performance of a two-link model of astronomical telescope. The proposed FLC utilizes the position deviation for the desired value, and its rate of change to regulate the armature voltage of the DC motor drive of each link. The final action of FLC is equivalent to PD controller with a variable gain by using an expert look- up table. This work presents the derivation of the mathematical model of 14 inch Celestron telescope and computer simulation of its motion. The FLC contains two groups of fuzzy sets.
Fuzzy logic and neural networks in artificial intelligence and pattern recognition
NASA Astrophysics Data System (ADS)
Sanchez, Elie
With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.
Fuzzy logic based intelligent control of a variable speed cage machine wind generation system
Simoes, M.G.; Bose, B.K.; Spiegel, R.J.
The paper describes a variable speed wind generation system where fuzzy logic principles are used for efficiency optimization and performance enhancement control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which pumps power to a utility grid or can supply to an autonomous system. The generation system has fuzzy logic control with vector control in the inner loops. A fuzzy controller tracks the generator speed with the wind velocity to extract the maximum power. A second fuzzy controller programs the machine flux for light load efficiency improvement, and a third fuzzy controller gives robust speed control against wind gust and turbine oscillatory torque. The complete control system has been developed, analyzed, and validated by simulation study. Performances have then been evaluated in detail.
Development of Fuzzy Logic and Soft Computing Methodologies
NASA Technical Reports Server (NTRS)
Zadeh, L. A.; Yager, R.
Our earlier research on computing with words (CW) has led to a new direction in fuzzy logic which points to a major enlargement of the role of natural languages in information processing, decision analysis and control. This direction is based on the methodology of computing with words and embodies a new theory which is referred to as the computational theory of perceptions (CTP). An important feature of this theory is that it can be added to any existing theory - especially to probability theory, decision analysis, and control - and enhance the ability of the theory to deal with real-world problems in which the decision-relevant information is a mixture of measurements and perceptions. The new direction is centered on an old concept - the concept of a perception - a concept which plays a central role in human cognition. The ability to reason with perceptions perceptions of time, distance, force, direction, shape, intent, likelihood, truth and other attributes of physical and mental objects - underlies the remarkable human capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Everyday examples of such tasks are parking a car, driving in city traffic, cooking a meal, playing golf and summarizing a story. Perceptions are intrinsically imprecise. Imprecision of perceptions reflects the finite ability of sensory organs and ultimately, the brain, to resolve detail and store information. More concretely, perceptions are both fuzzy and granular, or, for short, f-granular. Perceptions are f-granular in the sense that: (a) the boundaries of perceived classes are not sharply defined; and (b) the elements of classes are grouped into granules, with a granule being a clump of elements drawn together by indistinguishability, similarity. proximity or functionality. F-granularity of perceptions may be viewed as a human way of achieving data compression. In large measure, scientific progress has been, and continues to be
Trade-offs between fuel economy and NOx emissions using fuzzy logic control.
Rousseau, Aymeric; Saglini, Sylvain; Jakov, Michael; Gray, Donald; Hardy, Keith
The Center for Transportation Research at the Argonne National Laboratory (ANL) supports the DOE by evaluating advanced automotive technologies in a systems context. ANL has developed a unique set of compatible simulation tools and test equipment to perform an integrated systems analysis project from modeling through hardware testing and validation. This project utilized these capabilities to demonstrate the trade-off in fuel economy and Oxides of Nitrogen (NOx) emissions in a so-called ''pre-transmission'' parallel hybrid powertrain. The powertrain configuration (in simulation and on the dynamometer) consists of a Compression Ignition Direct Ignition (CIDI) engine, a Continuously Variable Transmission (CVT) and an electric drive motor coupled to the CVT input shaft. The trade-off is studied in a simulated environment using PSAT{copyright} with different controllers (fuzzy logic and rule based) and engine models (neural network and steady state models developed from ANL data).
Poisson's ratio prediction through dual stimulated fuzzy logic by ACE and GA-PS
NASA Astrophysics Data System (ADS)
Bagheripour, Parisa; Asoodeh, Mojtaba
Poisson's ratio is one of the most important rock mechanical parameters having significance in both planning and post analysis of wellbore operations. Laboratory measurement of this parameter covers a broad range of costs, including sidewall sampling, preservation, and laboratory tests. This study proposes an improved strategy, called dual stimulated fuzzy logic by ACE and GA-PS for determining Poisson's ratio from conventional well log data in a rapid, precise, and cost-effective way. Firstly, conventional well log data are transformed to a higher correlated data space with Poisson's ratio through the use of alternative condition expectation (ACE) algorithm. This step simplifies the convoluted space of the problem and makes it easier to solve for fuzzy logic. Subsequently, transformed conventional well log data are fed to fuzzy logic model. To ensure that optimal fuzzy model is constructed, a hybrid genetic algorithm-pattern search (GA-PS) technique is employed for extracting fuzzy clusters (or rules). This step sets fuzzy logic to its optimal performance. The propounded strategy was successfully applied to data from carbonate reservoir rocks of an Iranian Oil Field. A comparison between present model and previous models showed superiority of current study.
Methodological development of fuzzy-logic controllers from multivariable linear control.
Tso, S K; Fung, Y H
It is the function of the design of a fuzzy-logic controller to determine the universes of discourse of the antecedents and the consequents, number of membership labels, distribution and shape of membership functions, rule formulation, etc. Much of the information is usually extracted from expert knowledge, operator experience, or heuristic thinking. It is hence difficult to mechanize the first-stage design of fuzzy-logic controllers using linguistic labels whose performance is no worse than that of conventional multivariable linear controllers such as state-feedback controllers, PID controllers, etc. In this paper, an original systematic seven-step linear-to-fuzzy (LIN2FUZ) algorithm is proposed for generating the labels, universes of discourse of the antecedents and the consequents, and fuzzy rules of ;basically linear' fuzzy-logic controllers, given the reference design of available conventional multivariable linear controllers. The functionally equivalent fuzzy-logic controllers can thus provide the sound basis for the further development to achieve performance beyond the capability or the conventional controllers. The validity and effectiveness of the proposed LIN2FUZ algorithm are demonstrated by a four-input one-output inverted pendulum system. PMID:18255897
Can fuzzy logic bring complex problems into focus? Modeling imprecise factors in environmental policy
McKone, Thomas E.; Deshpande, Ashok W.
In modeling complex environmental problems, we often fail to make precise statements about inputs and outcome. In this case the fuzzy logic method native to the human mind provides a useful way to get at these problems. Fuzzy logic represents a significant change in both the approach to and outcome of environmental evaluations. Risk assessment is currently based on the implicit premise that probability theory provides the necessary and sufficient tools for dealing with uncertainty and variability. The key advantage of fuzzy methods is the way they reflect the human mind in its remarkable ability to store and process information which is consistently imprecise, uncertain, and resistant to classification. Our case study illustrates the ability of fuzzy logic to integrate statistical measurements with imprecise health goals. But we submit that fuzzy logic and probability theory are complementary and not competitive. In the world of soft computing, fuzzy logic has been widely used and has often been the ''smart'' behind smart machines. But it will require more effort and case studies to establish its niche in risk assessment or other types of impact assessment. Although we often hear complaints about ''bright lines,'' could we adapt to a system that relaxes these lines to fuzzy gradations? Would decision makers and the public accept expressions of water or air quality goals in linguistic terms with computed degrees of certainty? Resistance is likely. In many regions, such as the US and European Union, it is likely that both decision makers and members of the public are more comfortable with our current system in which government agencies avoid confronting uncertainties by setting guidelines that are crisp and often fail to communicate uncertainty. But some day perhaps a more comprehensive approach that includes exposure surveys, toxicological data, epidemiological studies coupled with fuzzy modeling will go a long way in resolving some of the conflict, divisiveness
Fuzzy-logic-based resource allocation for isolated and multiple platforms
NASA Astrophysics Data System (ADS)
Smith, James F., III; Rhyne, Robert D., II
Modern naval battle forces generally include many different platforms each with its own sensors, radar, ESM, and communications. The sharing of information measured by local sensors via communication links across the battle group should allow for optimal or near optimal decision. The survival of the battle group or members of the group depends on the automatic real-time allocation of various resources. A fuzzy logic algorithm has been developed that automatically allocates electronic attack resources in real- time. The particular approach to fuzzy logic that is used is the fuzzy decision tree, a generalization of the standard artificial intelligence technique of decision trees. The controller must be able to make decisions based on rules provided by experts. The fuzzy logic approach allows the direct incorporation of expertise forming a fuzzy linguistic description, i.e. a formal representation of the system in terms of fuzzy if-then rules. Genetic algorithm based optimization is conducted to determine the form of the membership functions for the fuzzy root concepts. The isolated platform and multi platform resource manager models are discussed as well as the underlying multi-platform communication model. The resource manager is shown to exhibit excellent performance under many demanding scenarios.
Integration of Genetic Algorithms and Fuzzy Logic for Urban Growth Modeling
NASA Astrophysics Data System (ADS)
Foroutan, E.; Delavar, M. R.; Araabi, B. N.
Urban growth phenomenon as a spatio-temporal continuous process is subject to spatial uncertainty. This inherent uncertainty cannot be fully addressed by the conventional methods based on the Boolean algebra. Fuzzy logic can be employed to overcome this limitation. Fuzzy logic preserves the continuity of dynamic urban growth spatially by choosing fuzzy membership functions, fuzzy rules and the fuzzification-defuzzification process. Fuzzy membership functions and fuzzy rule sets as the heart of fuzzy logic are rather subjective and dependent on the expert. However, due to lack of a definite method for determining the membership function parameters, certain optimization is needed to tune the parameters and improve the performance of the model. This paper integrates genetic algorithms and fuzzy logic as a genetic fuzzy system (GFS) for modeling dynamic urban growth. The proposed approach is applied for modeling urban growth in Tehran Metropolitan Area in Iran. Historical land use/cover data of Tehran Metropolitan Area extracted from the 1988 and 1999 Landsat ETM+ images are employed in order to simulate the urban growth. The extracted land use classes of the year 1988 include urban areas, street, vegetation areas, slope and elevation used as urban growth physical driving forces. Relative Operating Characteristic (ROC) curve as an fitness function has been used to evaluate the performance of the GFS algorithm. The optimum membership function parameter is applied for generating a suitability map for the urban growth. Comparing the suitability map and real land use map of 1999 gives the threshold value for the best suitability map which can simulate the land use map of 1999. The simulation outcomes in terms of kappa of 89.13% and overall map accuracy of 95.58% demonstrated the efficiency and reliability of the proposed model.
Fault classification by neural networks and fuzzy logic
Chwan-Hwa ``John`` Wu; Chihwen Li; Shih, H.; Alexion, C.C.; Ovick, N.L.; Murphy, J.H.
A neural fuzzy-based and a backpropagation neural network-based fault classifier for a three-phase motor will be described in this paper. In order to acquire knowledge, the neural fuzzy classifier incorporates a learning technique to automatically generate membership functions for fuzzy rules, and the backpropagation algorithm is used to train the neural network model. Therefore, in this paper, the preprocessing of signals, fuzzy and neural models, training methods, implementations for real-time response and testing results will be discussed in detail. Furthermore, the generalization capabilities of the neural fuzzy- and backpropagation-based classifiers for waveforms with varying magnitudes, frequencies, noises and positions of spikes and chops in a cycle of a sine wave will be investigated, and the computation requirements needed to achieve real-time response for both fuzzy and neural methods will be compared. {copyright} 1995 {ital American} {ital Institute} {ital of} {ital Physics}
Synthesis of nonlinear control strategies from fuzzy logic control algorithms
NASA Technical Reports Server (NTRS)
Langari, Reza
Fuzzy control has been recognized as an alternative to conventional control techniques in situations where the plant model is not sufficiently well known to warrant the application of conventional control techniques. Precisely what fuzzy control does and how it does what it does is not quite clear, however. This important issue is discussed and in particular it is shown how a given fuzzy control scheme can resolve into a nonlinear control law and that in those situations the success of fuzzy control hinges on its ability to compensate for nonlinearities in plant dynamics.
A new systematic and quantitative approach to characterization of surface nanostructures using fuzzy logic
NASA Astrophysics Data System (ADS)
Al-Mousa, Amjed A.
Thin films are essential constituents of modern electronic devices and have a multitude of applications in such devices. The impact of the surface morphology of thin films on the device characteristics where these films are used has generated substantial attention to advanced film characterization techniques. In this work, we present a new approach to characterize surface nanostructures of thin films by focusing on isolating nanostructures and extracting quantitative information, such as the shape and size of the structures. This methodology is applicable to any Scanning Probe Microscopy (SPM) data, such as Atomic Force Microscopy (AFM) data which we are presenting here. The methodology starts by compensating the AFM data for some specific classes of measurement artifacts. After that, the methodology employs two distinct techniques. The first, which we call the overlay technique, proceeds by systematically processing the raster data that constitute the scanning probe image in both vertical and horizontal directions. It then proceeds by classifying points in each direction separately. Finally, the results from both the horizontal and the vertical subsets are overlaid, where a final decision on each surface point is made. The second technique, based on fuzzy logic, relies on a Fuzzy Inference Engine (FIE) to classify the surface points. Once classified, these points are clustered into surface structures. The latter technique also includes a mechanism which can consistently distinguish crowded surfaces from those with sparsely distributed structures and then tune the fuzzy technique system uniquely for that surface. Both techniques have been applied to characterize organic semiconductor thin films of pentacene on different substrates. Also, we present a case study to demonstrate the effectiveness of our methodology to identify quantitatively particle sizes of two specimens of gold nanoparticles of different nominal dimensions dispersed on a mica surface. A comparison
The fundamental Fuzzy logic operators and some complex boolean logic circuits implemented by the chromogenism of a spirooxazine.
Gentili, Pier Luigi
1,3-Dihydro-1,3,3-trimethyl-8'-nitro-spiro[2H-indole-2,3'-[3H]naphth[2,1-b][1,4]oxazine] (SpO) is a photochromic, acidichromic and metallochromic compound. Its chromogenic properties are characterized in acetonitrile, at room temperature. They are exploited to process both boolean and Fuzzy logic. By using HClO(4), AlCl(3) and Cu(ClO(4))(2) as chemical inputs, UV radiation as power supply, and the absorbance at specific wavelengths in the visible as optical output, SpO results in a five-states molecular switch whereby some complex boolean logic circuits are implemented. If the chemical inputs are varied in an analog manner, the solution of SpO assumes an infinite number of colours. Therefore, by choosing the RGB colour coordinates as optical outputs, the fundamental operators of the "infinite-valued" Fuzzy logic are implemented. Particularly, two Fuzzy logic systems are built upon a new defuzzification procedure imitating the way humans perceive colours. PMID:21997229
A fuzzy logic approach to modeling the underground economy in Taiwan
NASA Astrophysics Data System (ADS)
Yu, Tiffany Hui-Kuang; Wang, David Han-Min; Chen, Su-Jane
The size of the 'underground economy' (UE) is valuable information in the formulation of macroeconomic and fiscal policy. This study applies fuzzy set theory and fuzzy logic to model Taiwan's UE over the period from 1960 to 2003. Two major factors affecting the size of the UE, the effective tax rate and the degree of government regulation, are used. The size of Taiwan's UE is scaled and compared with those of other models. Although our approach yields different estimates, similar patterns and leading are exhibited throughout the period. The advantage of applying fuzzy logic is twofold. First, it can avoid the complex calculations in conventional econometric models. Second, fuzzy rules with linguistic terms are easy for human to understand.
Using fuzzy logic to integrate neural networks and knowledge-based systems
NASA Technical Reports Server (NTRS)
Yen, John
Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.
Design and Implementation of Takagi-Sugeno Fuzzy Logic Controller for Shunt Compensator
NASA Astrophysics Data System (ADS)
Singh, Alka; Badoni, Manoj
This paper describes the application of Takagi-Sugeno (TS) type fuzzy logic controller to a three-phase shunt compensator in power distribution system. The shunt compensator is used for power quality improvement and has the ability to provide reactive power compensation, reduce the level of harmonics in supply currents, power factor correction and load balancing. Additionally, it can also be used to regulate voltage at the point of common coupling (PCC). The paper discusses the design of TS fuzzy logic controller and its implementation based on only four rules. The smaller number of rules makes it suitable for experimental verification as compared to Mamdani fuzzy controller. A small laboratory prototype of the system is developed and the control algorithm is verified experimentally. The TS fuzzy controller is compared with the proportional integral based industrial controller and their performance is compared under a wide variation of dynamic load changes.
Modelling of the automatic stabilization system of the aircraft course by a fuzzy logic method
NASA Astrophysics Data System (ADS)
Mamonova, T.; Syryamkin, V.; Vasilyeva, T.
The problem of the present paper concerns the development of a fuzzy model of the system of an aircraft course stabilization. In this work modelling of the aircraft course stabilization system with the application of fuzzy logic is specified. Thus the authors have used the data taken for an ordinary passenger plane. As a result of the study the stabilization system models were realised in the environment of Matlab package Simulink on the basis of the PID-regulator and fuzzy logic. The authors of the paper have shown that the use of the method of artificial intelligence allows reducing the time of regulation to 1, which is 50 times faster than the time when standard receptions of the management theory are used. This fact demonstrates a positive influence of the use of fuzzy regulation.
Evaluating environmental sustainability: an integration of multiple-criteria decision-making and fuzzy logic.
Liu, Kevin F R
While pursuing economic development, countries around the world have become aware of the importance of environmental sustainability; therefore, the evaluation of environmental sustainability has become a significant issue. Traditionally, multiple-criteria decision-making (MCDM) was widely used as a way of evaluating environmental sustainability, Recently, several researchers have attempted to implement this evaluation with fuzzy logic since they recognized the assessment of environmental sustainability as a subjective judgment Intuition. This paper outlines a new evaluation-framework of environmental sustainability, which integrates fuzzy logic into MCDM. This evaluation-framework consists of 36 structured and 5 unstructured decision-points, wherein MCDM is used to handle the former and fuzzy logic serves for the latter, With the integrated evaluation-framework, the evaluations of environmental sustainability in 146 countries are calculated, ranked and clustered, and the evaluation results are very helpful to these countries, as they identify their obstacles towards environmental sustainability. PMID:17377731
Fuzzy logic switching of thyristor controlled braking resistor considering coordination with SVC
Hiyama, T.; Mishiro, M.; Kihara, H.; Ortmeyer, T.H.
This paper presents a new switching control scheme for braking resistors using a fuzzy logic to enhance overall stability of electric power systems. In addition, the coordination with an SVC is also considered to achieve a wider stable region. The braking resistor is set on one of the generator busbars, where the real power output from the generator is measured to determine the firing-angle of the thyristor switch. The switching control scheme is simple so as not to require heavy computation on the micro-computer based switching controller. An SVC is set on one of the busbars in the transmission system. The switching of the SVC is performed by using a similar fuzzy logic control scheme to the one for the BR. Simulation results show the effectiveness of the proposed fuzzy logic switching control scheme.
Design and performance evaluation of a fuzzy-logic-based variable-speed wind generation system
Simoes, M.G.; Bose, B.K.; Spiegel, R.J.
Artificial intelligence techniques, such as fuzzy logic, neural network, and genetic algorithm, are recently showing a lot of promise in the application of power electronic systems. The paper describes the control strategy development, design, and experimental performance evaluation of a fuzzy-logic-based variable-speed wind generation system that uses a cage-type induction generator and double-sided pulsewidth-modulated (PWM) converters. The system can feed a utility grid maintaining unity power factor at all conditions or can supply an autonomous load. The fuzzy-logic-based control of the system helps to optimize efficiency and enhance performance. A complete 3.5-kW generation system has been developed, designed, and thoroughly evaluated by laboratory tests, in order to validate the predicted performance improvements. The system gives excellent performance and can easily be translated to a larger size in the field.
Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control
NASA Astrophysics Data System (ADS)
Othman, Ahmed M.; El-arini, Mahdi M. M.; Ghitas, Ahmed; Fathy, Ahmed
In the recent years, the solar energy becomes one of the most important alternative sources of electric energy, so it is important to improve the efficiency and reliability of the photovoltaic (PV) systems. Maximum power point tracking (MPPT) plays an important role in photovoltaic power systems because it maximize the power output from a PV system for a given set of conditions, and therefore maximize their array efficiency. This paper presents a maximum power point tracker (MPPT) using Fuzzy Logic theory for a PV system. The work is focused on the well known Perturb and Observe (P&O) algorithm and is compared to a designed fuzzy logic controller (FLC). The simulation work dealing with MPPT controller; a DC/DC Ćuk converter feeding a load is achieved. The results showed that the proposed Fuzzy Logic MPPT in the PV system is valid.
Convergent method of and apparatus for distributed control of robotic systems using fuzzy logic
Feddema, John T.; Driessen, Brian J.; Kwok, Kwan S.
A decentralized fuzzy logic control system for one vehicle or for multiple robotic vehicles provides a way to control each vehicle to converge on a goal without collisions between vehicles or collisions with other obstacles, in the presence of noisy input measurements and a limited amount of compute-power and memory on board each robotic vehicle. The fuzzy controller demonstrates improved robustness to noise relative to an exact controller.
A fuzzy logic based spacecraft controller for six degree of freedom control and performance results
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Hoblit, Jeffrey; Jani, Yashvant
The development philosophy of the fuzzy logic controller is explained, details of the rules and membership functions used are given, and the early results of testing of the control system for a representative range of scenarios are reported. The fuzzy attitude controller was found capable of performing all rotational maneuvers, including rate hold and rate maneuvers. It handles all orbital perturbations very efficiently and is very responsive in correcting errors.
Fuzzy Logic: Toward Measuring Gottfredson's Concept of Occupational Social Space.
ERIC Educational Resources Information Center
Hesketh, Beryl; And Others
Investigated the application of fuzzy graphic rating scale to measurement of preferences for occupational sex type, prestige, and interests using Gottfredson's concept of occupational social space. Reported reliability and validity data with illustrative examples of respondents' interpretations of their own fuzzy ratings. Outlined counseling and…
Operationalization of clinical practice guidelines using fuzzy logic.
Liu, J. C.; Shiffman, R. N.
There are a number of obstacles to successful operationalization of clinical practice guidelines, including the difficulty in accurately representing a statement's decidability or an action's executability. Both require reasoning with incomplete and imprecise information, and we present one means of processing such information. We begin with a brief overview of fuzzy set theory, in which elements can have partial memberships in multiple sets. With fuzzy inferencing, these sets can be combined to create multiple conclusions, each with varying degrees of truth. We demonstrate a fuzzy model developed from a published clinical practice guideline on the management of first simple febrile seizures. Although the creation of fuzzy sets can be an arbitrary process, we believe that fuzzy inferencing is an effective tool for the expression of guideline recommendations, and that it can be useful for the management of imprecision and uncertainty. PMID:9357633
Robust Fault Detection Using Robust Z1 Estimation and Fuzzy Logic
NASA Technical Reports Server (NTRS)
Curry, Tramone; Collins, Emmanuel G., Jr.; Selekwa, Majura; Guo, Ten-Huei (Technical Monitor)
This research considers the application of robust Z(sub 1), estimation in conjunction with fuzzy logic to robust fault detection for an aircraft fight control system. It begins with the development of robust Z(sub 1) estimators based on multiplier theory and then develops a fixed threshold approach to fault detection (FD). It then considers the use of fuzzy logic for robust residual evaluation and FD. Due to modeling errors and unmeasurable disturbances, it is difficult to distinguish between the effects of an actual fault and those caused by uncertainty and disturbance. Hence, it is the aim of a robust FD system to be sensitive to faults while remaining insensitive to uncertainty and disturbances. While fixed thresholds only allow a decision on whether a fault has or has not occurred, it is more valuable to have the residual evaluation lead to a conclusion related to the degree of, or probability of, a fault. Fuzzy logic is a viable means of determining the degree of a fault and allows the introduction of human observations that may not be incorporated in the rigorous threshold theory. Hence, fuzzy logic can provide a more reliable and informative fault detection process. Using an aircraft flight control system, the results of FD using robust Z(sub 1) estimation with a fixed threshold are demonstrated. FD that combines robust Z(sub 1) estimation and fuzzy logic is also demonstrated. It is seen that combining the robust estimator with fuzzy logic proves to be advantageous in increasing the sensitivity to smaller faults while remaining insensitive to uncertainty and disturbances.
Total Organic Carbon prediction in shale gas reservoirs using fuzzy logic
NASA Astrophysics Data System (ADS)
Ouadfeul, Sid-Ali; Aliouane, Leila
Here, we suggest the use the fuzzy logic approach for the prediction of the Total Organic Carbon (TOC) from well-logs data in shale gas reservoirs, two models are used for the estimation of the TOC from well-logs data; the first one is called the Schmoker's model while the second one is called the Passey's model. Scmocker's model requires the continuous measurement of the Bulk density, in case of absence of the bulk density measurement the Schmoker's model is not able to predict the TOC. In this case we suggest the use fuzzy logic system able to predict the total organic carbon in shale gas formations. The input of the fuzzy system is the four raw well-logs data measurements corresponding to the natural gamma ray, the neutron porosity, the slowness of the primary and shear waves. The desired output is the calculated TOC using the Schmoker's model. Application to well-logs data of two horizontal wells drilled in the lower Barnett shale clearly shows the ability of the fuzzy logic approach to suggest values of the total organic carbon in case of no bulk density measurement. Keywords TOC, Schmoker's model, Fuzzy logic, shale gas, Barnett shale, prediction.
Multi-objective decision-making under uncertainty: Fuzzy logic methods
NASA Technical Reports Server (NTRS)
Hardy, Terry L.
Selecting the best option among alternatives is often a difficult process. This process becomes even more difficult when the evaluation criteria are vague or qualitative, and when the objectives vary in importance and scope. Fuzzy logic allows for quantitative representation of vague or fuzzy objectives, and therefore is well-suited for multi-objective decision-making. This paper presents methods employing fuzzy logic concepts to assist in the decision-making process. In addition, this paper describes software developed at NASA Lewis Research Center for assisting in the decision-making process. Two diverse examples are used to illustrate the use of fuzzy logic in choosing an alternative among many options and objectives. One example is the selection of a lunar lander ascent propulsion system, and the other example is the selection of an aeration system for improving the water quality of the Cuyahoga River in Cleveland, Ohio. The fuzzy logic techniques provided here are powerful tools which complement existing approaches, and therefore should be considered in future decision-making activities.
Automated Interpretation of LIBS Spectra using a Fuzzy Logic Inference Engine
Jeremy J. Hatch; Timothy R. McJunkin; Cynthia Hanson; Jill R. Scott
Automated interpretation of laser-induced breakdown spectroscopy (LIBS) data is necessary due to the plethora of spectra that can be acquired in a relatively short time. However, traditional chemometric and artificial neural network methods that have been employed are not always transparent to a skilled user. A fuzzy logic approach to data interpretation has now been adapted to LIBS spectral interpretation. A fuzzy logic inference engine (FLIE) was used to differentiate between various copper containing and stainless steel alloys as well as unknowns. Results using FLIE indicate a high degree of confidence in spectral assignment.
Fuzzy logic and a risk-based graded approach for developing S/RIDs: An introduction
Wayland, J.R.
A Standards/Requirements Identification Document (S/RID) is the set of expressed performance expectations, or standards, for a facility. Critical to the development of an integrated standards-based management is the identification of a set of necessary and sufficient standards from a selected set of standards/requirements. There is a need for a formal, rigorous selection process for the S/RIDs. This is the first of three reports that develop a fuzzy logic selection process. In this report the fundamentals of fuzzy logic are discussed as they apply to a risk-based graded approach.
Fuzzy-logic-based safety verification framework for nuclear power plants.
Rastogi, Achint; Gabbar, Hossam A
This article presents a practical implementation of a safety verification framework for nuclear power plants (NPPs) based on fuzzy logic where hazard scenarios are identified in view of safety and control limits in different plant process values. Risk is estimated quantitatively and compared with safety limits in real time so that safety verification can be achieved. Fuzzy logic is used to define safety rules that map hazard condition with required safety protection in view of risk estimate. Case studies are analyzed from NPP to realize the proposed real-time safety verification framework. An automated system is developed to demonstrate the safety limit for different hazard scenarios. PMID:23020592
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection
Thounaojam, Dalton Meitei; Khelchandra, Thongam; Singh, Kh. Manglem; Roy, Sudipta
This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter. PMID:27127500
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection.
Thounaojam, Dalton Meitei; Khelchandra, Thongam; Manglem Singh, Kh; Roy, Sudipta
This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter. PMID:27127500
A genetic algorithms approach for altering the membership functions in fuzzy logic controllers
NASA Technical Reports Server (NTRS)
Shehadeh, Hana; Lea, Robert N.
Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.
Fuzzy logic: A "simple" solution for complexities in neurosciences?
Godil, Saniya Siraj; Shamim, Muhammad Shahzad; Enam, Syed Ather; Qidwai, Uvais
Background: Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum. Methods: This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology. Results: The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures. Conclusions: In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences. PMID:21541006
Using fuzzy logic analysis for siting decisions of infiltration trenches for highway runoff control.
Ki, Seo Jin; Ray, Chittaranjan
Determining optimal locations for best management practices (BMPs), including their field considerations and limitations, plays an important role for effective stormwater management. However, these issues have been often overlooked in modeling studies that focused on downstream water quality benefits. This study illustrates the methodology of locating infiltration trenches at suitable locations from spatial overlay analyses which combine multiple layers that address different aspects of field application into a composite map. Using seven thematic layers for each analysis, fuzzy logic was employed to develop a site suitability map for infiltration trenches, whereas the DRASTIC method was used to produce a groundwater vulnerability map on the island of Oahu, Hawaii, USA. In addition, the analytic hierarchy process (AHP), one of the most popular overlay analyses, was used for comparison to fuzzy logic. The results showed that the AHP and fuzzy logic methods developed significantly different index maps in terms of best locations and suitability scores. Specifically, the AHP method provided a maximum level of site suitability due to its inherent aggregation approach of all input layers in a linear equation. The most eligible areas in locating infiltration trenches were determined from the superposition of the site suitability and groundwater vulnerability maps using the fuzzy AND operator. The resulting map successfully balanced qualification criteria for a low risk of groundwater contamination and the best BMP site selection. The results of the sensitivity analysis showed that the suitability scores were strongly affected by the algorithms embedded in fuzzy logic; therefore, caution is recommended with their use in overlay analysis. Accordingly, this study demonstrates that the fuzzy logic analysis can not only be used to improve spatial decision quality along with other overlay approaches, but also is combined with general water quality models for initial and refined
Landslide susceptibility zonation in part of Tehri reservoir region using frequency ratio, fuzzy logic and GIS
NASA Astrophysics Data System (ADS)
Kumar, Rohan; Anbalagan, R.
A comprehensive study for the identification of landslide susceptible zones using landslide frequency ratio and fuzzy logic in GIS environment is presented for Tehri reservoir rim region (Uttarakhand, India). Temporal remote sensing data was used to prepare important landslide causative factor layers and landslide inventory. Primary and secondary topographic attributes namely slope, aspect, relative relief, profile curvature, topographic wetness index, and stream power index, were derived from digital elevation model. Landslide frequency ratio technique was adopted to correlate factors with landslides. Further, fuzzy logic method was applied for the integration of factors (causative factor) to map landslide susceptible zones. Normalized landslide frequency ratio value was used for the fuzzy membership function and different fuzzy operators were considered for the preparation of landslide susceptibility/hazard index map. The factors considered in this study were found to be carrying a wide range of information. Accordingly, a methodology was evolved to integrate the factors using combined fuzzy gamma and fuzzy OR operation. Fuzzy gamma integration was performed for six different gamma values (range: 0-1). Gamma value of 0.95 was selected for the preparation of final susceptibility map. Landslide susceptibility index map was divided into the following five hazard zones - very low, low, moderate, high, and very high - on the basis of natural break classification. Validation of the model was performed by using cumulative percentage curve technique. Area under curve value of cumulative percentage curve of proposed landslide susceptibility map (gamma = 0.95) was found to be 0.834 and it can be said that 83.4% accuracy was achieved by applying combined fuzzy logic and landslide frequency ratio method.
Automated synthesis of distillation sequences using fuzzy logic and simulation
Flowers, T.L.; Harrison, B.K.; Niccolai, M.J. )
An automated distillation sequencing system (DSEQSYS) is presented, which consists of three components: a control program, a fuzzy heuristic synthesis program, and a process simulator. DSEQSYS, when applied to problems previously reported in the literature, overcomes some of the disadvantages of using heuristics or mathematical programming alone. DSEQSYS can address problems involving nonsharp separations, nonideal chemical behavior, and conflicting heuristics. A simple approach for converting the traditional separation heuristics into corresponding fuzzy heuristics is also demonstrated.
Fuzzy, crisp, and human logic in e-commerce marketing data mining
NASA Astrophysics Data System (ADS)
Hearn, Kelda L.; Zhang, Yanqing
In today's business world there is an abundance of available data and a great need to make good use of it. Many businesses would benefit from examining customer habits and trends and making marketing and product decisions based on that analysis. However, the process of manually examining data and making sound decisions based on that data is time consuming and often impractical. Intelligent systems that can make judgments similar to human judgments are sorely needed. Thus, systems based on fuzzy logic present themselves as an option to be seriously considered. The work described in this paper attempts to make an initial comparison between fuzzy logic and more traditional hard or crisp logic to see which would make a better substitute for human intervention. In this particular case study, customers are classified into categories that indicate how desirable the customer would be as a prospect for marketing. This classification is based on a small set of customer data. The results from these investigations make it clear that fuzzy logic is more able to think for itself and make decisions that more closely match human decision and is therefore significantly closer to human logic than crisp logic.
Modeling and simulation of evacuation behavior using fuzzy logic in a goal finding application
NASA Astrophysics Data System (ADS)
Sharma, Sharad; Ogunlana, Kola; Sree, Swetha
Modeling and simulation has been widely used as a training and educational tool for depicting different evacuation strategies and damage control decisions during evacuation. However, there are few simulation environments that can include human behavior with low to high levels of fidelity. It is well known that crowd stampede induced by panic leads to fatalities as people are crushed or trampled. Our proposed goal finding application can be used to model situations that are difficult to test in real-life due to safety considerations. It is able to include agent characteristics and behaviors. Findings of this model are very encouraging as agents are able to assume various roles to utilize fuzzy logic on the way to reaching their goals. Fuzzy logic is used to model stress, panic and the uncertainty of emotions. The fuzzy rules link these parts together while feeding into behavioral rules. The contributions of this paper lies in our approach of utilizing fuzzy logic to show learning and adaptive behavior of agents in a goal finding application. The proposed application will aid in running multiple evacuation drills for what-if scenarios by incorporating human behavioral characteristics that can scale from a room to building. Our results show that the inclusion of fuzzy attributes made the evacuation time of the agents closer to the real time drills.
A Measurement-Theoretic Analysis of the Fuzzy Logic Model of Perception.
ERIC Educational Resources Information Center
Crowther, Court S.; And Others
The fuzzy logic model of perception (FLMP) is analyzed from a measurement-theoretic perspective. The choice rule of FLMP is shown to be equivalent to a version of the Rasch model. In fact, FLMP can be reparameterized as a simple two-category logit model. (SLD)
A Comparison of Neural Networks and Fuzzy Logic Methods for Process Modeling
NASA Technical Reports Server (NTRS)
Cios, Krzysztof J.; Sala, Dorel M.; Berke, Laszlo
The goal of this work was to analyze the potential of neural networks and fuzzy logic methods to develop approximate response surfaces as process modeling, that is for mapping of input into output. Structural response was chosen as an example. Each of the many methods surveyed are explained and the results are presented. Future research directions are also discussed.
Fuzzy Logic Controlled Solar Module for Driving Three- Phase Induction Motor
NASA Astrophysics Data System (ADS)
Afiqah Zainal, Nurul; Sooi Tat, Chan; Ajisman
Renewable energy produced by solar module gives advantages for generated three- phase induction motor in remote area. But, solar module's ou tput is uncertain and complex. Fuzzy logic controller is one of controllers that can handle non-linear system and maximum power of solar module. Fuzzy logic controller used for Maximum Power Point Tracking (MPPT) technique to control Pulse-Width Modulation (PWM) for switching power electronics circuit. DC-DC boost converter used to boost up photovoltaic voltage to desired output and supply voltage source inverter which controlled by three-phase PWM generated by microcontroller. IGBT switched Voltage source inverter (VSI) produced alternating current (AC) voltage from direct current (DC) source to control speed of three-phase induction motor from boost converter output. Results showed that, the output power of solar module is optimized and controlled by using fuzzy logic controller. Besides that, the three-phase induction motor can be drive and control using VSI switching by the PWM signal generated by the fuzzy logic controller. This concluded that the non-linear system can be controlled and used in driving three-phase induction motor.
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic.
Li, Ning; Martínez, José-Fernán; Hernández Díaz, Vicente
Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters' dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively. PMID:26266412
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic
Li, Ning; Martínez, José-Fernán; Díaz, Vicente Hernández
Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters' dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively. PMID:26266412
Fuzzy logic controllers for electric motors and wind turbines. Report for October 1996-April 1997
Spiegel, R.J.
The paper discusses a precision laboratory test facility that has been assempbled to test the performance of two fuzzy-logic based controllers for electric motors and wind turbines. Commercial induction motors up to 10 hp (7.46 kWe) in motors and equipped with adjustable-speed drives (ASDs) were used to test the motor optimizers.
A "fuzzy"-logic language for encoding multiple physical traits in biomolecules.
Warszawski, Shira; Netzer, Ravit; Tawfik, Dan S; Fleishman, Sarel J
To carry out their activities, biological macromolecules balance different physical traits, such as stability, interaction affinity, and selectivity. How such often opposing traits are encoded in a macromolecular system is critical to our understanding of evolutionary processes and ability to design new molecules with desired functions. We present a framework for constraining design simulations to balance different physical characteristics. Each trait is represented by the equilibrium fractional occupancy of the desired state relative to its alternatives, ranging from none to full occupancy, and the different traits are combined using Boolean operators to effect a "fuzzy"-logic language for encoding any combination of traits. In another paper, we presented a new combinatorial backbone design algorithm AbDesign where the fuzzy-logic framework was used to optimize protein backbones and sequences for both stability and binding affinity in antibody-design simulation. We now extend this framework and find that fuzzy-logic design simulations reproduce sequence and structure design principles seen in nature to underlie exquisite specificity on the one hand and multispecificity on the other hand. The fuzzy-logic language is broadly applicable and could help define the space of tolerated and beneficial mutations in natural biomolecular systems and design artificial molecules that encode complex characteristics. PMID:25311857
The paper describes a variable-speed wind generation system where fuzzy logic principles are used to optimize efficiency and enhance performance control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which either pump...
The report gives results of a demonstration of the successful application of fuzzy logic to enhance the performance and control of a variable-speed wind generation system. A squirrel cage induction generator feeds the power to either a double-sided pulse-width modulation converte...
This paper describes a fuzzy-logic-based energy optimizing controller to improve the efficiency of induction motor/drives operating at various load (torque) and speed conditions. Improvement of induction motor efficiency is important not only from the considerations of energy sav...
Multi-Objective Trajectory Optimization by a Hierarchical Gradient Algorithm with Fuzzy Decision Logic
NASA Astrophysics Data System (ADS)
Kojima, Hirohisa; Nakajima, Nobuyuki
The rest-to-rest maneuver problem of the flexible space structure is the two point boundary value problem (TPBVP) and is solved by some gradient methods. If TPBVP is strongly restricted by the constraints, TBVP becomes ill-defined problem, and the solution meeting all constraints cannot be obtained. However, reasonable suboptimal solutions are often needed since real plants are necessary to be controlled. In order to obtain such suboptimal solutions, we have developed a modified version of the hierarchy gradient method by installing fuzzy decision logic. Constraints are classified into non-fuzzy constraints and fuzzy constraints according to their priorities. Fuzzy constraints having trade-off relationship with each other are compromised reasonably by fuzzy decision logic. The usefulness of the proposed method is numerically and experimentally demonstrated by applying to the rest-to-rest slew maneuver problem of a flexible space structure, where fuzzy constraints are final time, sensitivity of residual vibration energy with respect to the structure frequency uncertainty, and maximum bending moment at the root of flexible appendage.
Fuzzy logic techniques for rendezvous and docking of two geostationary satellites
NASA Technical Reports Server (NTRS)
Ortega, Guillermo
Large assemblings in space require the ability to manage rendezvous and docking operations. In future these techniques will be required for the gradual build up of big telecommunication platforms in the geostationary orbit. The paper discusses the use of fuzzy logic to model and implement a control system for the docking/berthing of two satellites in geostationary orbit. The system mounted in a chaser vehicle determines the actual state of both satellites and generates torques to execute maneuvers to establish the structural latching. The paper describes the proximity operations to collocate the two satellites in the same orbital window, the fuzzy guidance and navigation of the chaser approaching the target and the final Fuzzy berthing. The fuzzy logic system represents a knowledge based controller that realizes the close loop operations autonomously replacing the conventional control algorithms. The goal is to produce smooth control actions in the proximity of the target and during the docking to avoid disturbance torques in the final assembly orbit. The knowledge of the fuzzy controller consists of a data base of rules and the definitions of the fuzzy sets. The knowledge of an experienced spacecraft controller is captured into a set of rules forming the Rules Data Base.
Fuzzy Logic Path Planning System for Collision Avoidance by an Autonomous Rover Vehicle
NASA Technical Reports Server (NTRS)
Murphy, Michael G.
Systems already developed at JSC have shown the benefits of applying fuzzy logic control theory to space related operations. Four major issues are addressed that are associated with developing an autonomous collision avoidance subsystem within a path planning system designed for application in a remote, hostile environment that does not lend itself well to remote manipulation of the vehicle involved through Earth-based telecommunication. A good focus for this is unmanned exploration of the surface of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. The four major issues addressed are: (1) avoidance of a single fuzzy moving obstacle; (2) back off from a dead end in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system.
VANET Broadcast Protocol Based on Fuzzy Logic and Lightweight Retransmission Mechanism
NASA Astrophysics Data System (ADS)
Wu, Celimuge; Ohzahata, Satoshi; Kato, Toshihiko
Vehicular ad hoc networks have been attracting the interest of both academic and industrial communities on account of their potential role in Intelligent Transportation Systems (ITS). However, due to vehicle movement and fading in wireless communications, providing a reliable and efficient multi-hop broadcast service in vehicular ad hoc networks is still an open research topic. In this paper, we propose FUZZBR (FUZZy BRoadcast), a fuzzy logic based multi-hop broadcast protocol for information dissemination in vehicular ad hoc networks. FUZZBR has low message overhead since it uses only a subset of neighbor nodes to relay data messages. In the relay node selection, FUZZBR jointly considers multiple metrics of inter-vehicle distance, node mobility and signal strength by employing the fuzzy logic. FUZZBR also uses a lightweight retransmission mechanism to retransmit a packet when a relay fails. We use computer simulations to evaluate the performance of FUZZBR.
Fuzzy Logic-Supported Detection of Complex Geospatial Features in a Web Service Environment
NASA Astrophysics Data System (ADS)
He, L. L.; Di, L. P.; Yue, P.; Zhang, M. D.
Spatial relations among simple features can be used to characterize complex geospatial features. These spatial relations are often represented using linguistic terms such as near, which have inherent vagueness and imprecision. Fuzzy logic can be used to modeling fuzziness of the terms. Once simple features are extracted from remote sensing imagery, degree of satisfaction of spatial relations among these simple features can be derived to detect complex features. The derivation process can be performed in a distributed service environment, which benefits Earth science society in the last decade. Workflow-based service can provide ondemand uncertainty-aware discovery of complex features in a distributed environment. A use case on the complex facility detection illustrates the applicability of the fuzzy logic-supported service-oriented approach.
Hybrid neural network and fuzzy logic approaches for rendezvous and capture in space
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.; Castellano, Timothy
The nonlinear behavior of many practical systems and unavailability of quantitative data regarding the input-output relations makes the analytical modeling of these systems very difficult. On the other hand, approximate reasoning-based controllers which do not require analytical models have demonstrated a number of successful applications such as the subway system in the city of Sendai. These applications have mainly concentrated on emulating the performance of a skilled human operator in the form of linguistic rules. However, the process of learning and tuning the control rules to achieve the desired performance remains a difficult task. Fuzzy Logic Control is based on fuzzy set theory. A fuzzy set is an extension of a crisp set. Crisp sets only allow full membership or no membership at all, whereas fuzzy sets allow partial membership. In other words, an element may partially belong to a set.
Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli
Morris, Melody K.; Saez-Rodriguez, Julio; Clarke, David C.; Sorger, Peter K.; Lauffenburger, Douglas A.
Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone. PMID:21408212
Construction of a fuzzy and Boolean logic gates based on DNA.
Zadegan, Reza M; Jepsen, Mette D E; Hildebrandt, Lasse L; Birkedal, Victoria; Kjems, Jørgen
Logic gates are devices that can perform logical operations by transforming a set of inputs into a predictable single detectable output. The hybridization properties, structure, and function of nucleic acids can be used to make DNA-based logic gates. These devices are important modules in molecular computing and biosensing. The ideal logic gate system should provide a wide selection of logical operations, and be integrable in multiple copies into more complex structures. Here we show the successful construction of a small DNA-based logic gate complex that produces fluorescent outputs corresponding to the operation of the six Boolean logic gates AND, NAND, OR, NOR, XOR, and XNOR. The logic gate complex is shown to work also when implemented in a three-dimensional DNA origami box structure, where it controlled the position of the lid in a closed or open position. Implementation of multiple microRNA sensitive DNA locks on one DNA origami box structure enabled fuzzy logical operation that allows biosensing of complex molecular signals. Integrating logic gates with DNA origami systems opens a vast avenue to applications in the fields of nanomedicine for diagnostics and therapeutics. PMID:25565140
Summary report: A preliminary investigation into the use of fuzzy logic for the control of redundant manipulators
NASA Technical Reports Server (NTRS)
Cheatham, John B., Jr.; Magee, Kevin N.
The Rice University Department of Mechanical Engineering and Materials Sciences' Robotics Group designed and built an eight degree of freedom redundant manipulator. Fuzzy logic was proposed as a control scheme for tasks not directly controlled by a human operator. In preliminary work, fuzzy logic control was implemented for a camera tracking system and a six degree of freedom manipulator. Both preliminary systems use real time vision data as input to fuzzy controllers. Related projects include integration of tactile sensing and fuzzy control of a redundant snake-like arm that is under construction.
Building Better Discipline Strategies for Schools by Fuzzy Logics
ERIC Educational Resources Information Center
Chang, Dian-Fu; Juan, Ya-Yun; Chou, Wen-Ching
This study aims to realize better discipline strategies for applying in high schools. We invited 400 teachers to participate the survey and collected their perceptions on the discipline strategies in terms of the acceptance of strategies and their effectiveness in schools. Based on the idea of fuzzy statistics, this study transformed the fuzzy…
Fluorescence intensity positivity classification of Hep-2 cells images using fuzzy logic
NASA Astrophysics Data System (ADS)
Sazali, Dayang Farzana Abang; Janier, Josefina Barnachea; May, Zazilah Bt.
Indirect Immunofluorescence (IIF) is a good standard used for antinuclear autoantibody (ANA) test using Hep-2 cells to determine specific diseases. Different classifier algorithm methods have been proposed in previous works however, there still no valid set as a standard to classify the fluorescence intensity. This paper presents the use of fuzzy logic to classify the fluorescence intensity and to determine the positivity of the Hep-2 cell serum samples. The fuzzy algorithm involves the image pre-processing by filtering the noises and smoothen the image, converting the red, green and blue (RGB) color space of images to luminosity layer, chromaticity layer "a" and "b" (LAB) color space where the mean value of the lightness and chromaticity layer "a" was extracted and classified by using fuzzy logic algorithm based on the standard score ranges of antinuclear autoantibody (ANA) fluorescence intensity. Using 100 data sets of positive and intermediate fluorescence intensity for testing the performance measurements, the fuzzy logic obtained an accuracy of intermediate and positive class as 85% and 87% respectively.
Analysis of atomic force microscopy data for surface characterization using fuzzy logic
Al-Mousa, Amjed; Niemann, Darrell L.; Niemann, Devin J.; Gunther, Norman G.; Rahman, Mahmud
In this paper we present a methodology to characterize surface nanostructures of thin films. The methodology identifies and isolates nanostructures using Atomic Force Microscopy (AFM) data and extracts quantitative information, such as their size and shape. The fuzzy logic based methodology relies on a Fuzzy Inference Engine (FIE) to classify the data points as being top, bottom, uphill, or downhill. The resulting data sets are then further processed to extract quantitative information about the nanostructures. In the present work we introduce a mechanism which can consistently distinguish crowded surfaces from those with sparsely distributed structures and present an omni-directional search technique to improve the structural recognition accuracy. In order to demonstrate the effectiveness of our approach we present a case study which uses our approach to quantitatively identify particle sizes of two specimens each with a unique gold nanoparticle size distribution. - Research Highlights: {yields} A Fuzzy logic analysis technique capable of characterizing AFM images of thin films. {yields} The technique is applicable to different surfaces regardless of their densities. {yields} Fuzzy logic technique does not require manual adjustment of the algorithm parameters. {yields} The technique can quantitatively capture differences between surfaces. {yields} This technique yields more realistic structure boundaries compared to other methods.
Obstacle detection for mobile vehicle using neural network and fuzzy logic
NASA Astrophysics Data System (ADS)
Sun, Huaijiang; Yang, Jingyu
In our mobile vehicle project, sensors for environment modeling are a CCD color camera and two line-scan laser range finders. The CCD color camera is used to detect road edges. The two line-scan laser range finders are used to detect obstacles. Only two line-scan laser range finders increase processing speed, but there are blind zones for low obstacles, especially near the vehicle. In this paper, neural network and fuzzy logic are used to cluster and fuse obstacle points provided by two line-scan laser range finders. There is an assumption that obstacles missed by laser radar in some instant must be detected previously. A circle Adaptive Resonance neural network algorithm is used to incrementally cluster obstacle points provided by laser range finders into candidate obstacles. Every candidate obstacle is expressed by a circle, and is assigned a belief by a fuzzy logic system. Inputs of the fuzzy logic system are radius and number of points. Fuzzy rules are provided by human and can be fine-tuned with training data. The final true obstacle is the nearest one chosen from candidate obstacles whose beliefs exceed a threshold. Experiment results indicate that our mobile vehicle can safely follow road and avoid obstacles.
Quantifying favorableness for occurrence of a mineral deposit type using fuzzy logic; an example from Arizona
Gettings, Mark E.; Bultman, Mark W.
An application of possibility theory from fuzzy logic to the quantification of favorableness for quartz-carbonate vein deposits in the southern Santa Rita Mountains of southeastern Arizona is described. Three necessary but probably not sufficient conditions for the formation of these deposits were defined as the occurrence of carbonate berain rocks within hypabyssal depths, significant fracturing of the rocks, and proximity to a felsic intrusive. The quality of data available to evaluate these conditions is variable over the study area. The possibility of each condition was represented as a fuzzy set enumerated over the area. The intersection of the sets measures the degree of simultaneous occurrence of hte necessary factors and provides a measure of the possibility of deposit occurrence. Using fuzzy set technicques, the effect of one or more fuzzy sets relative to the others in the intersection can be controlled and logical combinations of the sets can be used to impose a time sequential constraint on the necessary conditions. Other necessary conditions, and supplementary conditions such as variable data quality or intensity of exploration can be included in the analysis by their proper representation as fuzzy sets.
Segmentation method of eye region based on fuzzy logic system for classifying open and closed eyes
NASA Astrophysics Data System (ADS)
Kim, Ki Wan; Lee, Won Oh; Kim, Yeong Gon; Hong, Hyung Gil; Lee, Eui Chul; Park, Kang Ryoung
The classification of eye openness and closure has been researched in various fields, e.g., driver drowsiness detection, physiological status analysis, and eye fatigue measurement. For a classification with high accuracy, accurate segmentation of the eye region is required. Most previous research used the segmentation method by image binarization on the basis that the eyeball is darker than skin, but the performance of this approach is frequently affected by thick eyelashes or shadows around the eye. Thus, we propose a fuzzy-based method for classifying eye openness and closure. First, the proposed method uses I and K color information from the HSI and CMYK color spaces, respectively, for eye segmentation. Second, the eye region is binarized using the fuzzy logic system based on I and K inputs, which is less affected by eyelashes and shadows around the eye. The combined image of I and K pixels is obtained through the fuzzy logic system. Third, in order to reflect the effect by all the inference values on calculating the output score of the fuzzy system, we use the revised weighted average method, where all the rectangular regions by all the inference values are considered for calculating the output score. Fourth, the classification of eye openness or closure is successfully made by the proposed fuzzy-based method with eye images of low resolution which are captured in the environment of people watching TV at a distance. By using the fuzzy logic system, our method does not require the additional procedure of training irrespective of the chosen database. Experimental results with two databases of eye images show that our method is superior to previous approaches.
Challenges And Results of the Applications of Fuzzy Logic in the Classification of Rich Galaxy Clusters
NASA Astrophysics Data System (ADS)
Santiago Girola Schneider, Rafael
The fuzzy logic is a branch of the artificial intelligence founded on the concept that 'everything is a matter of degree.' It intends to create mathematical approximations on the resolution of certain types of problems. In addition, it aims to produce exact results obtained from imprecise data, for which it is particularly useful for electronic and computer applications. This enables it to handle vague or unspecific information when certain parts of a system are unknown or ambiguous and, therefore, they cannot be measured in a reliable manner. Also, when the variation of a variable can produce an alteration on the others.The main focus of this paper is to prove the importance of these techniques formulated from a theoretical analysis on its application on ambiguous situations in the field of the rich clusters of galaxies. The purpose is to show its applicability in the several classification systems proposed for the rich clusters, which are based on criteria such as the level of richness of the cluster, the distribution of the brightest galaxies, whether there are signs of type-cD galaxies or not or the existence of sub-clusters.Fuzzy logic enables the researcher to work with "imprecise" information implementing fuzzy sets and combining rules to define actions. The control systems based on fuzzy logic join input variables that are defined in terms of fuzzy sets through rule groups that produce one or several output values of the system under study. From this context, the application of the fuzzy logic's techniques approximates the solution of the mathematical models in abstractions about the rich galaxy cluster classification of physical properties in order to solve the obscurities that must be confronted by an investigation group in order to make a decision.
Fuzzy-logic-based LLRF control for the RFT-30 cyclotron
NASA Astrophysics Data System (ADS)
Kong, Young-Bae; Lee, Eun-Je; Hur, Min-Goo; Park, Jeong-Hoon; Park, Yong-Dae; Yang, Seung-Dae; Jung, In-Su; Park, Yeun-Soo
A RFT-30 cyclotron can be used for various applications such as radioisotope production and fundamental research. A low level radio frequency (LLRF) system adjusts the parameters for stable operation of the radio frequency (RF) system. It is important for the LLRF system to maintain a stable resonance condition during its operation. In this paper, we propose a fuzzy-based LLRF control for the RFT-30 cyclotron. The proposed approach stabilizes the resonance condition by moving the fine tuner based on a fuzzy logic controller (FLC). Performance results show that the FLC approach maintains a stable resonance condition for the RF system.
Using Fuzzy Logic for Performance Evaluation in Reinforcement Learning
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.; Khedkar, Pratap S.
Current reinforcement learning algorithms require long training periods which generally limit their applicability to small size problems. A new architecture is described which uses fuzzy rules to initialize its two neural networks: a neural network for performance evaluation and another for action selection. This architecture is applied to control of dynamic systems and it is demonstrated that it is possible to start with an approximate prior knowledge and learn to refine it through experiments using reinforcement learning.
A fuzzy logic based approach to direct load control
Bhattacharyya, K.; Crow, M.L.
Demand side management programs are strategies designed to alter the shape of the load curve. In order to successfully implement such a strategy, customer acceptance of the program is vital. It is thus desirable to design a model for direct load control which may accommodate customer preferences. This paper presents a methodology for optimizing both customer satisfaction and utility unit commitment savings, based on a fuzzy load model for the direct load control of appliances.
Application of fuzzy logic in intelligent software agents for IP selection
NASA Astrophysics Data System (ADS)
Liu, Jian; Shragowitz, Eugene B.
IPs (Intellectual Properties) are becoming increasingly essential in today's electronic system design. One of important issues in design reuse is the IP selection, i.e. finding an existing solution that matches the user's expectations best. This paper describes the Internet-based intelligent software system (Software Agent) that helps the user to pick out the optimal designs among those marketed by the IP vendors. The Software Agent for IP Selection (SAFIPS) conducts dialogues with both the IP users and IP vendors, narrowing the choices after evaluating general characteristics first, followed by matching behavioral, RTL, logic, and physical levels. The SAFIPS system conducts reasoning based on fuzzy logic rules derived in the process of dialogues of the software agent with the IP users and vendors. In addition to the dialogue system and fuzzy logic inference system, the SAFIPS includes a HDL simulator and fuzzy logic evaluator that are used to measure the level of matching of the user's behavioral model with the IP vendor's model.
Fuzzy propositions weighted by veracities or how to relate fuzzy logic and probability theory for segmentation of ultrasound images
NASA Astrophysics Data System (ADS)
Debon, Renaud; Solaiman, Basel; Cauvin, Jean-Michel; Robaszkiewicz, Michel; Roux, Christian
In medical imaging, and more generally in medical information, researches go towards fusion systems. Nowadays, the steps of information source definition, the pertinent data extraction and the fusion need to be conducted as a whole. In this work, our interest is related to the esophagus wall segmentation from ultrasound images sequences. We aim to elaborate a general methodology of data mining that coherently links works on data selection and fusion architectures, in order to extract useful information from raw data and to integrate efficiently the physician a prior. In the presented method, based on fuzzy logic, some fuzzy propositions are defined using physicians a prior knowledge. The use of probabilistic distributions, estimated thanks to a learning base of pathologic and non-pathologic cases, enables the veracity of these propositions to be qualified. This promising idea enables information to be managed through the consideration of both information imprecision and uncertainty. In the same time, the obtained benefit, when a prior knowledge source is injected in a fusion based decision system, can be quantified. By considering that, the fuzzyfication stage is optimized relatively to a given criteria using a genetic algorithm. By this manner, fuzzy sets corresponding to the physicians ambiguous a prior are defined objectively. At this level, we successively compare performances obtained when fuzzy functions are defined empirically and when they are optimized. We conclude this paper with the first results on esophagus wall segmentation and outline some further works.
Integrating fuzzy logic and statistics to improve the reliable delimitation of biogeographic regions and transition zones.
Olivero, Jesús; Márquez, Ana L; Real, Raimundo
This study uses the amphibian species of the Mediterranean basin to develop a consistent procedure based on fuzzy sets with which biogeographic regions and biotic transition zones can be objectively detected and reliably mapped. Biogeographical regionalizations are abstractions of the geographical organization of life on Earth that provide frameworks for cataloguing species and ecosystems, for answering basic questions in biogeography, evolutionary biology, and systematics, and for assessing priorities for conservation. On the other hand, limits between regions may form sharply defined boundaries along some parts of their borders, whereas elsewhere they may consist of broad transition zones. The fuzzy set approach provides a heuristic way to analyse the complexity of the biota within an area; significantly different regions are detected whose mutual limits are sometimes fuzzy, sometimes clearly crisp. Most of the regionalizations described in the literature for the Mediterranean biogeographical area present a certain degree of convergence when they are compared within the context of fuzzy interpretation, as many of the differences found between regionalizations are located in transition zones, according to our case study. Compared with other classification procedures based on fuzzy sets, the novelty of our method is that both fuzzy logic and statistics are used together in a synergy in order to avoid arbitrary decisions in the definition of biogeographic regions and transition zones. PMID:22744774
Controlling chaos in a defined trajectory using adaptive fuzzy logic algorithm
NASA Astrophysics Data System (ADS)
Sadeghi, Maryam; Menhaj, Bagher
Chaos is a nonlinear behavior of chaotic system with the extreme sensitivity to the initial conditions. Chaos control is so complicated that solutions never converge to a specific numbers and vary chaotically from one amount to the other next. A tiny perturbation in a chaotic system may result in chaotic, periodic, or stationary behavior. Modern controllers are introduced for controlling the chaotic behavior. In this research an adaptive Fuzzy Logic Controller (AFLC) is proposed to control the chaotic system with two equilibrium points. This method is introduced as an adaptive progressed fashion with the full ability to control the nonlinear systems even in the undertrained conditions. Using AFLC designers are released to determine the precise mathematical model of system and satisfy the vast adaption that is needed for a rapid variation which may be caused in the dynamic of nonlinear system. Rules and system parameters are generated through the AFLC and expert knowledge is downright only in the initialization stage. So if the knowledge was not assuring the dynamic of system it could be changed through the adaption procedure of parameters values. AFLC methodology is an advanced control fashion in control yielding to both robustness and smooth motion in nonlinear system control.
A Fuzzy Logic Based Controller for the Automated Alignment of a Laser-beam-smoothing Spatial Filter
NASA Technical Reports Server (NTRS)
Krasowski, M. J.; Dickens, D. E.
A fuzzy logic based controller for a laser-beam-smoothing spatial filter is described. It is demonstrated that a human operator's alignment actions can easily be described by a system of fuzzy rules of inference. The final configuration uses inexpensive, off-the-shelf hardware and allows for a compact, readily implemented embedded control system.
Fuzzy logic algorithm for quantitative tissue characterization of diffuse liver diseases from ultrasound images.
Badawi, A M; Derbala, A S; Youssef, A M
Computerized ultrasound tissue characterization has become an objective means for diagnosis of liver diseases. It is difficult to differentiate diffuse liver diseases, namely cirrhotic and fatty liver by visual inspection from the ultrasound images. The visual criteria for differentiating diffused diseases are rather confusing and highly dependent upon the sonographer's experience. This often causes a bias effects in the diagnostic procedure and limits its objectivity and reproducibility. Computerized tissue characterization to assist quantitatively the sonographer for the accurate differentiation and to minimize the degree of risk is thus justified. Fuzzy logic has emerged as one of the most active area in classification. In this paper, we present an approach that employs Fuzzy reasoning techniques to automatically differentiate diffuse liver diseases using numerical quantitative features measured from the ultrasound images. Fuzzy rules were generated from over 140 cases consisting of normal, fatty, and cirrhotic livers. The input to the fuzzy system is an eight dimensional vector of feature values: the mean gray level (MGL), the percentile 10%, the contrast (CON), the angular second moment (ASM), the entropy (ENT), the correlation (COR), the attenuation (ATTEN) and the speckle separation. The output of the fuzzy system is one of the three categories: cirrhosis, fatty or normal. The steps done for differentiating the pathologies are data acquisition and feature extraction, dividing the input spaces of the measured quantitative data into fuzzy sets. Based on the expert knowledge, the fuzzy rules are generated and applied using the fuzzy inference procedures to determine the pathology. Different membership functions are developed for the input spaces. This approach has resulted in very good sensitivities and specificity for classifying diffused liver pathologies. This classification technique can be used in the diagnostic process, together with the history
Investigation of the Flutter Suppression by Fuzzy Logic Control for Hypersonic Wing
NASA Astrophysics Data System (ADS)
Li, Dongxu; Luo, Qing; Xu, Rui
This paper presents a fundamental study of flutter characteristics and control performance of an aeroelastic system based on a two-dimensional double wedge wing in the hypersonic regime. Dynamic equations were established based on the modified third order nonlinear piston theory and some nonlinear structural effects are also included. A set of important parameters are observed. And then aeroelastic control law is designed to suppress the amplitude of the LCOs for the system in the sub/supercritical speed range by applying fuzzy logic control on the input of the deflection of the flap. The overall effects of the parameters on the aeroelastic system were outlined. Nonlinear aeroelastic responses in the open- and closed-loop system are obtained through numerical methods. The simulations show fuzzy logic control methods are effective in suppressing flutter and provide a smart approach for this complicated system.
A Genetic Algorithm Optimised Fuzzy Logic Controller for Automatic Generation Control for Single Area System
NASA Astrophysics Data System (ADS)
Saini, J. S.; Jain, V.
This paper presents a genetic algorithm (GA)-based design and optimization of fuzzy logic controller (FLC) for automatic generation control (AGC) for a single area. FLCs are characterized by a set of parameters, which are optimized using GA to improve their performance. The design of input and output membership functions (mfs) of an FLC is carried out by automatically tuning (off-line) the parameters of the membership functions. Tuning is based on maximization of a comprehensive fitness function constructed as inverse of a weighted average of three performance indices, i.e., integral square deviation (ISD), the integral of square of the frequency deviation and peak overshoot (Mp), and settling time (ts). The GA-optimized FLC (GAFLC) shows better performance as compared to a conventional proportional integral (PI) and a hand-designed fuzzy logic controller not only for a standard system (displaying frequency deviations) but also under parametric and load disturbances.
Fuzzy logic merger of spectral and ecological information for improved montane forest mapping.
White, Joseph D.; Running, Steven W.; Ryan, Kevin C.; Key, Carl H.
Environmental data are often utilized to guide interpretation of spectral information based on context, however, these are also important in deriving vegetation maps themselves, especially where ecological information can be mapped spatially. A vegetation classification procedure is presented which combines a classification of spectral data from Landsat‐5 Thematic Mapper (TM) and environmental data based on topography and fire history. These data were combined utilizing fuzzy logic where assignment of each pixel to a single vegetation category was derived comparing the partial membership of each vegetation category within spectral and environmental classes. Partial membership was assigned from canopy cover for forest types measured from field sampling. Initial classification of spectral and ecological data produced map accuracies of less than 50% due to overlap between spectrally similar vegetation and limited spatial precision for predicting local vegetation types solely from the ecological information. Combination of environmental data through fuzzy logic increased overall mapping accuracy (70%) in coniferous forest communities of northwestern Montana, USA.
Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree
Kim, Jaekwon; Lee, Jongsik
Objectives The importance of the prediction of coronary heart disease (CHD) has been recognized in Korea; however, few studies have been conducted in this area. Therefore, it is necessary to develop a method for the prediction and classification of CHD in Koreans. Methods A model for CHD prediction must be designed according to rule-based guidelines. In this study, a fuzzy logic and decision tree (classification and regression tree [CART])-driven CHD prediction model was developed for Koreans. Datasets derived from the Korean National Health and Nutrition Examination Survey VI (KNHANES-VI) were utilized to generate the proposed model. Results The rules were generated using a decision tree technique, and fuzzy logic was applied to overcome problems associated with uncertainty in CHD prediction. Conclusions The accuracy and receiver operating characteristic (ROC) curve values of the propose systems were 69.51% and 0.594, proving that the proposed methods were more efficient than other models. PMID:26279953
A scene-based nonuniformity correction algorithm based on fuzzy logic
NASA Astrophysics Data System (ADS)
Huang, Jun; Ma, Yong; Fan, Fan; Mei, Xiaoguang; Liu, Zhe
Scene-based nonuniformity correction algorithms based on the LMS adaptive filter are quite efficient to reduce the fixed pattern noise in infrared images. They are famous for their low cost of computation and storage recourses. Unfortunately, ghosting artifacts can be easily introduced in edge areas when the inter-frame motion slows. In this paper, a gated scene-based nonuniformity correction algorithm is proposed. A novel low-pass filter based on the fuzzy logic is proposed to estimate the true scene radiation as the desired signal in the LMS adaptive filter. The fuzzy logic can also evaluate the probability that a pixel and its locals belong to edge areas. Then the update of the correction parameters for the pixels in edge areas can be gated. The experiment results show that our method is reliable and the ghosting artifacts are reduced.
Fuzzy-logic-based hybrid locomotion mode classification for an active pelvis orthosis: Preliminary results.
Yuan, Kebin; Parri, Andrea; Yan, Tingfang; Wang, Long; Munih, Marko; Vitiello, Nicola; Wang, Qining
In this paper, we present a fuzzy-logic-based hybrid locomotion mode classification method for an active pelvis orthosis. Locomotion information measured by the onboard hip joint angle sensors and the pressure insoles is used to classify five locomotion modes, including two static modes (sitting, standing still), and three dynamic modes (level-ground walking, ascending stairs, and descending stairs). The proposed method classifies these two kinds of modes first by monitoring the variation of the relative hip joint angle between the two legs within a specific period. Static states are then classified by the time-based absolute hip joint angle. As for dynamic modes, a fuzzy-logic based method is proposed for the classification. Preliminary experimental results with three able-bodied subjects achieve an off-line classification accuracy higher than 99.49%. PMID:26737144
Adaptive fuzzy logic restriction rules for error correction and safe stimulation patterns during functional electrical stimulation.
Hansen, M; Haugland, M K
Adaptive restriction rules based on fuzzy logic have been developed to eliminate errors and to increase stimulation safety in the foot-drop correction application, specifically when using adaptive logic networks to provide a stimulation control signal based on neural activity recorded from peripheral sensory nerve branches. The fuzzy rules were designed to increase flexibility and offer easier customization, compared to earlier versions of restriction rules. The rules developed quantified the duration of swing and stance phases into states of accepting or rejecting new transitions, based on the cyclic nature of gait and statistics on the current gait patterns. The rules were easy to custom design for a specific application, using linguistic terms to model the actions of the rules. The rules were tested using pre-recorded gait data processed through a gait event detector and proved to reduce detection delay and the number of errors, compared to conventional rules. PMID:11601442
A fuzzy logic intelligent diagnostic system for spacecraft integrated vehicle health management
NASA Technical Reports Server (NTRS)
Wu, G. Gordon
Due to the complexity of future space missions and the large amount of data involved, greater autonomy in data processing is demanded for mission operations, training, and vehicle health management. In this paper, we develop a fuzzy logic intelligent diagnostic system to perform data reduction, data analysis, and fault diagnosis for spacecraft vehicle health management applications. The diagnostic system contains a data filter and an inference engine. The data filter is designed to intelligently select only the necessary data for analysis, while the inference engine is designed for failure detection, warning, and decision on corrective actions using fuzzy logic synthesis. Due to its adaptive nature and on-line learning ability, the diagnostic system is capable of dealing with environmental noise, uncertainties, conflict information, and sensor faults.
A knowledge-based approach to ECG interpretation using fuzzy logic.
Kundu, M; Nasipuri, M; Basu, D K
A rule-based expert system which uses generalized modus ponens (GMP) from fuzzy logic as a rule of inference is described here for classification of abnormalities related to rhythm disorder in the human heart, through interpretation of the patient's electrocardiographic (EGG) patterns. Application of GMP makes diagnosis of a wide range of variations in the input ECG patterns possible even if they differ from the patterns defined in the preconditions of the rules of the rulebase. The work shows how fuzzy logic with suitably drawn possibility distributions of variables of cardiological domain plays a significant role in making the expert system sensitive to finer variations of input ECG patterns, which are very common in bioelectric signals, without enhancing the size of the rulebase. PMID:18255941
Sleep apnea detection using an adaptive fuzzy logic based screening system.
Morsy, Ahmed A; Al-Ashmouny, Khaled M
We report an adaptive diagnostic system for the classification of breathing events for the purpose of detecting sleep apnea syndromes. The system employs two classification engines used in series. The first engine is fuzzy logic-based and generates one of three outcomes for each breathing event: normal, abnormal, and not-sure. The second classification engine is based on a center of gravity engine which is trained using the normal and abnormal events, generated by the first engine, and is specifically designed for sorting out the not-sure events. The fuzzy logic engine can be tuned very conservatively to reduce or eliminate the chance of error at the first stage. Since the second engine is trained adaptively using normal and abnormal data of the same patient, its accuracy is generally better than relying on multi-patient training approaches. The two-step, adaptive nature of the system allows for high accuracy and lends itself well for practical implementation. PMID:17281661
Fuzzy logic electric vehicle regenerative antiskid braking and traction control system
Cikanek, Susan R.
An regenerative antiskid braking and traction control system using fuzzy logic for an electric or hybrid vehicle having a regenerative braking system operatively connected to an electric traction motor, and a separate hydraulic braking system includes sensors for monitoring present vehicle parameters and a processor, responsive to the sensors, for calculating vehicle parameters defining the vehicle behavior not directly measurable by the sensor and determining if regenerative antiskid braking control, requiring hydraulic braking control, and requiring traction control are required. The processor then employs fuzzy logic based on the determined vehicle state and provides command signals to a motor controller to control operation of the electric traction motor and to the brake controller to control fluid pressure applied at each vehicle wheel to provide the appropriate regenerative braking control, hydraulic braking control, and traction control.
Fuzzy logic electric vehicle regenerative antiskid braking and traction control system
Cikanek, S.R.
An regenerative antiskid braking and traction control system using fuzzy logic for an electric or hybrid vehicle having a regenerative braking system operatively connected to an electric traction motor, and a separate hydraulic braking system includes sensors for monitoring present vehicle parameters and a processor, responsive to the sensors, for calculating vehicle parameters defining the vehicle behavior not directly measurable by the sensor and determining if regenerative antiskid braking control, requiring hydraulic braking control, and requiring traction control are required. The processor then employs fuzzy logic based on the determined vehicle state and provides command signals to a motor controller to control operation of the electric traction motor and to the brake controller to control fluid pressure applied at each vehicle wheel to provide the appropriate regenerative braking control, hydraulic braking control, and traction control. 123 figs.
The stock-flow model of spatial data infrastructure development refined by fuzzy logic.
Abdolmajidi, Ehsan; Harrie, Lars; Mansourian, Ali
The system dynamics technique has been demonstrated to be a proper method by which to model and simulate the development of spatial data infrastructures (SDI). An SDI is a collaborative effort to manage and share spatial data at different political and administrative levels. It is comprised of various dynamically interacting quantitative and qualitative (linguistic) variables. To incorporate linguistic variables and their joint effects in an SDI-development model more effectively, we suggest employing fuzzy logic. Not all fuzzy models are able to model the dynamic behavior of SDIs properly. Therefore, this paper aims to investigate different fuzzy models and their suitability for modeling SDIs. To that end, two inference and two defuzzification methods were used for the fuzzification of the joint effect of two variables in an existing SDI model. The results show that the Average-Average inference and Center of Area defuzzification can better model the dynamics of SDI development. PMID:27006876
Flight test results of the fuzzy logic adaptive controller-helicopter (FLAC-H)
NASA Astrophysics Data System (ADS)
Wade, Robert L.; Walker, Gregory W.
The fuzzy logic adaptive controller for helicopters (FLAC-H) demonstration is a cooperative effort between the US Army Simulation, Training, and Instrumentation Command (STRICOM), the US Army Aviation and Troop Command, and the US Army Missile Command to demonstrate a low-cost drone control system for both full-scale and sub-scale helicopters. FLAC-H was demonstrated on one of STRICOM's fleet of full-scale rotary-winged target drones. FLAC-H exploits fuzzy logic in its flight control system to provide a robust solution to the control of the helicopter's dynamic, nonlinear system. Straight forward, common sense fuzzy rules governing helicopter flight are processed instead of complex mathematical models. This has resulted in a simplified solution to the complexities of helicopter flight. Incorporation of fuzzy logic reduced the cost of development and should also reduce the cost of maintenance of the system. An adaptive algorithm allows the FLAC-H to 'learn' how to fly the helicopter, enabling the control system to adjust to varying helicopter configurations. The adaptive algorithm, based on genetic algorithms, alters the fuzzy rules and their related sets to improve the performance characteristics of the system. This learning allows FLAC-H to automatically be integrated into a new airframe, reducing the development costs associated with altering a control system for a new or heavily modified aircraft. Successful flight tests of the FLAC-H on a UH-1H target drone were completed in September 1994 at the White Sands Missile Range in New Mexico. This paper discuses the objective of the system, its design, and performance.
Adaptively Managing Wildlife for Climate Change: A Fuzzy Logic Approach
NASA Astrophysics Data System (ADS)
Prato, Tony
Wildlife managers have little or no control over climate change. However, they may be able to alleviate potential adverse impacts of future climate change by adaptively managing wildlife for climate change. In particular, wildlife managers can evaluate the efficacy of compensatory management actions (CMAs) in alleviating potential adverse impacts of future climate change on wildlife species using probability-based or fuzzy decision rules. Application of probability-based decision rules requires managers to specify certain probabilities, which is not possible when they are uncertain about the relationships between observed and true ecological conditions for a species. Under such uncertainty, the efficacy of CMAs can be evaluated and the best CMA selected using fuzzy decision rules. The latter are described and demonstrated using three constructed cases that assume: (1) a single ecological indicator (e.g., population size for a species) in a single time period; (2) multiple ecological indicators for a species in a single time period; and (3) multiple ecological conditions for a species in multiple time periods.
Ensemble of ground subsidence hazard maps using fuzzy logic
NASA Astrophysics Data System (ADS)
Park, Inhye; Lee, Jiyeong; Saro, Lee
Hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok, Korea, were constructed using fuzzy ensemble techniques and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, groundwater, and ground subsidence maps. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 70/30 for training and validation of the models. The relationships between the detected ground-subsidence area and the factors were identified and quantified by frequency ratio (FR), logistic regression (LR) and artificial neural network (ANN) models. The relationships were used as factor ratings in the overlay analysis to create ground-subsidence hazard indexes and maps. The three GSH maps were then used as new input factors and integrated using fuzzy-ensemble methods to make better hazard maps. All of the hazard maps were validated by comparison with known subsidence areas that were not used directly in the analysis. As the result, the ensemble model was found to be more effective in terms of prediction accuracy than the individual model.
Modeling coastal environmental changes by fuzzy logic approach
NASA Astrophysics Data System (ADS)
Zoran, Maria A.; Zoran, Liviu Florin V.
The coastal zone contains that unique environmental triple point where the water, land and atmospheric components of the terrestrial surface converge and interact. This paper is an application of remotely sensed images in marine coastal land cover classification for change detection assessment. The nature of the gradients in coastal region land cover composition among the map classes can therefore be identified.A supervised approach uses the prior knowledge about the area and thus it is very useful in getting better results than an unsupervised classification. The study test area was North-Western Black Sea coastal region, characterized by no so fast drastic changes,as it is a slow and continuous process. Satellite images (Landsat MSS, TM, ETM, SAR ERS, ASTER, MODIS) over a period of time between 1975 and 2003 were chosen for change detection analysis.In the fuzzy approach, it is possible to describe change as a degree, this being the main reason for fuzzy approach using for classification and change detection of major land cover classes in a marine coastal area.The results can be utilized as a temporal land-use change model for a region to quantify the extent and nature of change, and aid in future prediction studies, which helps in planning environmental agencies to develop sustainable land-use practices .
A fuzzy logic controller for hormone administration using an implantable pump
NASA Technical Reports Server (NTRS)
Coles, L. Stephen; Wells, George H., Jr.
This paper describes the requirements for a Fuzzy Logic Controller for the physiologic administration of hormones by means of a FDA-approved surgically implantable infusion pump. Results of a LabVIEW computer simulation for the administration of insulin for diabetic adult patients as well as human growth hormone for pediatric patients are presented. A VHS video tape of the simulation in action has been prepared and is available for viewing.
Mathematical Modeling of spatial disease variables by Spatial Fuzzy Logic for Spatial Decision Support Systems
NASA Astrophysics Data System (ADS)
Platz, M.; Rapp, J.; Groessler, M.; Niehaus, E.; Babu, A.; Soman, B.
A Spatial Decision Support System (SDSS) provides support for decision makers and should not be viewed as replacing human intelligence with machines. Therefore it is reasonable that decision makers are able to use a feature to analyze the provided spatial decision support in detail to crosscheck the digital support of the SDSS with their own expertise. Spatial decision support is based on risk and resource maps in a Geographic Information System (GIS) with relevant layers e.g. environmental, health and socio-economic data. Spatial fuzzy logic allows the representation of spatial properties with a value of truth in the range between 0 and 1. Decision makers can refer to the visualization of the spatial truth of single risk variables of a disease. Spatial fuzzy logic rules that support the allocation of limited resources according to risk can be evaluated with measure theory on topological spaces, which allows to visualize the applicability of this rules as well in a map. Our paper is based on the concept of a spatial fuzzy logic on topological spaces that contributes to the development of an adaptive Early Warning And Response System (EWARS) providing decision support for the current or future spatial distribution of a disease. It supports the decision maker in testing interventions based on available resources and apply risk mitigation strategies and provide guidance tailored to the geo-location of the user via mobile devices. The software component of the system would be based on open source software and the software developed during this project will also be in the open source domain, so that an open community can build on the results and tailor further work to regional or international requirements and constraints. A freely available EWARS Spatial Fuzzy Logic Demo was developed wich enables a user to visualize risk and resource maps based on individual data in several data formats.
Environmental impact assessment procedure: A new approach based on fuzzy logic
Peche, Roberto; Rodriguez, Esther
The information related to the different environmental impacts produced by the execution of activities and projects is often limited, described by semantic variables and, affected by a high degree of inaccuracy and uncertainty, thereby making fuzzy logic a suitable tool with which to express and treat this information. The present study proposes a new approach based on fuzzy logic to carry out the environmental impact assessment (EIA) of these activities and projects. Firstly, a set of impact properties is stated and two nondimensional parameters - ranging from 0 to 100 -are assigned, (p{sub i}) to assess the value of the property and (v{sub i}) to assess its contribution to each environmental impact. Next, the impact properties are described by means of fuzzy numbers p{sub i}{sup -} using generalised confidence intervals. Then, a procedure based on fuzzy arithmetic is developed to define the assessment functions v-bar = f(p-bar) - conventional mathematical functions, which incorporate the knowledge of these impact properties and give the fuzzy values v{sub i}{sup -} corresponding to each p{sub i}{sup -}. Subsequently, the fuzzy value of each environmental impact V-bar is estimated by aggregation of the values v{sub i}{sup -}, in order to obtain the total positive and negative environmental impacts V{sup +-} and V{sup --} and, later - from them - the total environmental impact of the activity or project TV{sup -}. Finally, the defuzzyfication of TV{sup -} leads to a punctual impact estimator TV{sup (1)} - a conventional EI estimation - and its corresponding uncertainty interval estimator left brace(delta{sub l}(TV{sup -}),delta{sub r}(TV{sup -})right brace, which represent the total value of the environmental impact caused by the execution of the considered activity or project.
A fuzzy logic methodology for fault-tree analysis in critical safety systems
Erbay, A.; Ikonomopoulos, A. )
A new approach for fault-tree analysis in critical safety systems employing fuzzy sets for information representation is presented in this paper. The methodology is based on the utilization of the extension principle for mapping crisp measurements to various degrees of membership in the fuzzy set of linguistic Truth. Criticality alarm systems are used in miscellaneous nuclear fuel processing, handling, and storage facilities to reduce the risk associated with fissile material operations. Fault-tree methodologies are graphic illustrations of tile failure logic associated with the development of a particular system failure (top event) from basic subcomponent failures (primary events). The term event denotes a dynamic change of state that occurs to system elements, which may include hardware, software, human, or environmental factors. A fault-tree represents a detailed, deductive, analysis that requires extensive system information. The knowledge incorporated in a fault tree can be articulated in logical rules of the form [open quotes]IF A is true THEN B is true.[close quotes] However, it is well known that this type of syllogism fails to give an answer when the satisfaction of the antecedent clause is only partial. Zadeh suggested a new type of fuzzy conditional inference. This type of syllogism (generalized modus ponens) reads as follows: Premise: A is partially true Implication: IF A is true THEN B is true Conclusion: B is partially-true. In generalized modus ponens, the antecedent is true only to some degree; hence, it is desired to compute the grade to which the consequent is satisfied. Fuzzy sets provide a natural environment for this type of computation because fuzzy variables (e.g., B) can take fuzzy values (e.g., partially-true).
Fuzzy-logic based strategy for validation of multiplex methods: example with qualitative GMO assays.
Bellocchi, Gianni; Bertholet, Vincent; Hamels, Sandrine; Moens, W; Remacle, José; Van den Eede, Guy
This paper illustrates the advantages that a fuzzy-based aggregation method could bring into the validation of a multiplex method for GMO detection (DualChip GMO kit, Eppendorf). Guidelines for validation of chemical, bio-chemical, pharmaceutical and genetic methods have been developed and ad hoc validation statistics are available and routinely used, for in-house and inter-laboratory testing, and decision-making. Fuzzy logic allows summarising the information obtained by independent validation statistics into one synthetic indicator of overall method performance. The microarray technology, introduced for simultaneous identification of multiple GMOs, poses specific validation issues (patterns of performance for a variety of GMOs at different concentrations). A fuzzy-based indicator for overall evaluation is illustrated in this paper, and applied to validation data for different genetically modified elements. Remarks were drawn on the analytical results. The fuzzy-logic based rules were shown to be applicable to improve interpretation of results and facilitate overall evaluation of the multiplex method. PMID:19533405
Use of fuzzy logic for condition monitoring of motor driven machineries
NASA Astrophysics Data System (ADS)
Janier, Josefina Barnachea; Zaim Zaharia, M. Fazrin
An intelligent system called Fuzzy Logic is one of the current technologies that allow a description of the desired system behavior using common language. It generalizes the yes-no Boolean logic into numerical value of 1 and 0 but also permits the in between values. This paper presents the use of Fuzzy Logic to determine the unusual increase of vibrations of an induction motor called vibration analysis. Sudden increase of vibrations could be a good indicator of faulty condition of the motor. Based on the vibration characteristics of the motor, a Fuzzy Inference System (FIS) was created. The system classified the motor of the gas distribution pump as `acceptable' of the vibration ranges from 1.8mm/s to 4.5mm/s or `monitor closely' of the vibration ranges from 4.5mm/s to 7.1mm/s respectively. The system enabled an early detection of faults which is very important in maintenance management.
Fuzzy Logic-Based Guaranteed Lifetime Protocol for Real-Time Wireless Sensor Networks
Shah, Babar; Iqbal, Farkhund; Abbas, Ali; Kim, Ki-Il
Few techniques for guaranteeing a network lifetime have been proposed despite its great impact on network management. Moreover, since the existing schemes are mostly dependent on the combination of disparate parameters, they do not provide additional services, such as real-time communications and balanced energy consumption among sensor nodes; thus, the adaptability problems remain unresolved among nodes in wireless sensor networks (WSNs). To solve these problems, we propose a novel fuzzy logic model to provide real-time communication in a guaranteed WSN lifetime. The proposed fuzzy logic controller accepts the input descriptors energy, time and velocity to determine each node's role for the next duration and the next hop relay node for real-time packets. Through the simulation results, we verified that both the guaranteed network's lifetime and real-time delivery are efficiently ensured by the new fuzzy logic model. In more detail, the above-mentioned two performance metrics are improved up to 8%, as compared to our previous work, and 14% compared to existing schemes, respectively. PMID:26295238
Towards Resilient Critical Infrastructures: Application of Type-2 Fuzzy Logic in Embedded Network Security Cyber Sensor
Ondrej Linda; Todd Vollmer; Jim Alves-Foss; Milos Manic
Resiliency and cyber security of modern critical infrastructures is becoming increasingly important with the growing number of threats in the cyber-environment. This paper proposes an extension to a previously developed fuzzy logic based anomaly detection network security cyber sensor via incorporating Type-2 Fuzzy Logic (T2 FL). In general, fuzzy logic provides a framework for system modeling in linguistic form capable of coping with imprecise and vague meanings of words. T2 FL is an extension of Type-1 FL which proved to be successful in modeling and minimizing the effects of various kinds of dynamic uncertainties. In this paper, T2 FL provides a basis for robust anomaly detection and cyber security state awareness. In addition, the proposed algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental cyber-security test-bed.
Fuzzy Logic-Based Guaranteed Lifetime Protocol for Real-Time Wireless Sensor Networks.
Shah, Babar; Iqbal, Farkhund; Abbas, Ali; Kim, Ki-Il
Few techniques for guaranteeing a network lifetime have been proposed despite its great impact on network management. Moreover, since the existing schemes are mostly dependent on the combination of disparate parameters, they do not provide additional services, such as real-time communications and balanced energy consumption among sensor nodes; thus, the adaptability problems remain unresolved among nodes in wireless sensor networks (WSNs). To solve these problems, we propose a novel fuzzy logic model to provide real-time communication in a guaranteed WSN lifetime. The proposed fuzzy logic controller accepts the input descriptors energy, time and velocity to determine each node's role for the next duration and the next hop relay node for real-time packets. Through the simulation results, we verified that both the guaranteed network's lifetime and real-time delivery are efficiently ensured by the new fuzzy logic model. In more detail, the above-mentioned two performance metrics are improved up to 8%, as compared to our previous work, and 14% compared to existing schemes, respectively. PMID:26295238
Profitability analysis of a femtosecond laser system for cataract surgery using a fuzzy logic approach
Trigueros, José Antonio; Piñero, David P; Ismail, Mahmoud M
AIM To define the financial and management conditions required to introduce a femtosecond laser system for cataract surgery in a clinic using a fuzzy logic approach. METHODS In the simulation performed in the current study, the costs associated to the acquisition and use of a commercially available femtosecond laser platform for cataract surgery (VICTUS, TECHNOLAS Perfect Vision GmbH, Bausch & Lomb, Munich, Germany) during a period of 5y were considered. A sensitivity analysis was performed considering such costs and the countable amortization of the system during this 5y period. Furthermore, a fuzzy logic analysis was used to obtain an estimation of the money income associated to each femtosecond laser-assisted cataract surgery (G). RESULTS According to the sensitivity analysis, the femtosecond laser system under evaluation can be profitable if 1400 cataract surgeries are performed per year and if each surgery can be invoiced more than $500. In contrast, the fuzzy logic analysis confirmed that the patient had to pay more per surgery, between $661.8 and $667.4 per surgery, without considering the cost of the intraocular lens (IOL). CONCLUSION A profitability of femtosecond laser systems for cataract surgery can be obtained after a detailed financial analysis, especially in those centers with large volumes of patients. The cost of the surgery for patients should be adapted to the real flow of patients with the ability of paying a reasonable range of cost. PMID:27500115
Performance Analysis of a Semiactive Suspension System with Particle Swarm Optimization and Fuzzy Logic Control
Qazi, Abroon Jamal; de Silva, Clarence W.
This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control. PMID:24574868
Fuzzy Logic based Handoff Latency Reduction Mechanism in Layer 2 of Heterogeneous Mobile IPv6 Networks
NASA Astrophysics Data System (ADS)
Anwar, Farhat; Masud, Mosharrof H.; Latif, Suhaimi A.
Mobile IPv6 (MIPv6) is one of the pioneer standards that support mobility in IPv6 environment. It has been designed to support different types of technologies for providing seamless communications in next generation network. However, MIPv6 and subsequent standards have some limitations due to its handoff latency. In this paper, a fuzzy logic based mechanism is proposed to reduce the handoff latency of MIPv6 for Layer 2 (L2) by scanning the Access Points (APs) while the Mobile Node (MN) is moving among different APs. Handoff latency occurs when the MN switches from one AP to another in L2. Heterogeneous network is considered in this research in order to reduce the delays in L2. Received Signal Strength Indicator (RSSI) and velocity of the MN are considered as the input of fuzzy logic technique. This technique helps the MN to measure optimum signal quality from APs for the speedy mobile node based on fuzzy logic input rules and makes a list of interfaces. A suitable interface from the list of available interfaces can be selected like WiFi, WiMAX or GSM. Simulation results show 55% handoff latency reduction and 50% packet loss improvement in L2 compared to standard to MIPv6.
Performance analysis of a semiactive suspension system with particle swarm optimization and fuzzy logic control.
Qazi, Abroon Jamal; de Silva, Clarence W; Khan, Afzal; Khan, Muhammad Tahir
This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control. PMID:24574868
Mathematical modeling of the lambda switch: a fuzzy logic approach.
Laschov, Dmitriy; Margaliot, Michael
Gene regulation plays a central role in the development and functioning of living organisms. Gaining a deeper qualitative and quantitative understanding of gene regulation is an important scientific challenge. The Lambda switch is commonly used as a paradigm of gene regulation. Verbal descriptions of the structure and functioning of the switch have appeared in biological textbooks. We apply fuzzy modeling to transform one such verbal description into a well-defined mathematical model. The resulting model is a piecewise-quadratic second-order differential equation. It demonstrates functional fidelity with known results while being simple enough to allow a rather detailed analysis. Properties such as the number, location, and domain of attraction of equilibrium points can be studied analytically. Furthermore, the model provides a rigorous explanation for the so-called stability puzzle of the Lambda switch. PMID:19589343
Polarimetric synthetic aperture radar image classification using fuzzy logic in the H/α-Wishart algorithm
NASA Astrophysics Data System (ADS)
Zhu, Teng; Yu, Jie; Li, Xiaojuan; Yang, Jie
To solve the problem that the H/α-Wishart unsupervised classification algorithm can generate only inflexible clusters due to arbitrarily fixed zone boundaries in the clustering processing, a refined fuzzy logic based classification scheme called the H/α-Wishart fuzzy clustering algorithm is proposed in this paper. A fuzzy membership function was developed for the degree of pixels belonging to each class instead of an arbitrary boundary. To devise a unified fuzzy function, a normalized Wishart distance is proposed during the clustering step in the new algorithm. Then the degree of membership is computed to implement fuzzy clustering. After an iterative procedure, the algorithm yields a classification result. The new classification scheme is applied to two L-band polarimetric synthetic aperture radar (PolSAR) images and an X-band high-resolution PolSAR image of a field in LingShui, Hainan Province, China. Experimental results show that the classification precision of the refined algorithm is greater than that of the H/α-Wishart algorithm and that the refined algorithm performs well in differentiating shadows and water areas.
Fuzzy-logic modeling of Fenton's strong chemical oxidation process treating three types of landfill leachates.
Sari, Hanife; Yetilmezsoy, Kaan; Ilhan, Fatih; Yazici, Senem; Kurt, Ugur; Apaydin, Omer
Three multiple input and multiple output-type fuzzy-logic-based models were developed as an artificial intelligence-based approach to model a novel integrated process (UF-IER-EDBM-FO) consisted of ultrafiltration (UF), ion exchange resins (IER), electrodialysis with bipolar membrane (EDBM), and Fenton's oxidation (FO) units treating young, middle-aged, and stabilized landfill leachates. The FO unit was considered as the key process for implementation of the proposed modeling scheme. Four input components such as H(2)O(2)/chemical oxygen demand ratio, H(2)O(2)/Fe(2+) ratio, reaction pH, and reaction time were fuzzified in a Mamdani-type fuzzy inference system to predict the removal efficiencies of chemical oxygen demand, total organic carbon, color, and ammonia nitrogen. A total of 200 rules in the IF-THEN format were established within the framework of a graphical user interface for each fuzzy-logic model. The product (prod) and the center of gravity (centroid) methods were performed as the inference operator and defuzzification methods, respectively, for the proposed prognostic models. Fuzzy-logic predicted results were compared to the outputs of multiple regression models by means of various descriptive statistical indicators, and the proposed methodology was tested against the experimental data. The testing results clearly revealed that the proposed prognostic models showed a superior predictive performance with very high determination coefficients (R (2)) between 0.930 and 0.991. This study indicated a simple means of modeling and potential of a knowledge-based approach for capturing complicated inter-relationships in a highly non-linear problem. Clearly, it was shown that the proposed prognostic models provided a well-suited and cost-effective method to predict removal efficiencies of wastewater parameters prior to discharge to receiving streams. PMID:23247523
A new methodology of extraction, optimization and application of crisp and fuzzy logical rules.
Duch, W; Adamczak, R; Grabczewski, K
A new methodology of extraction, optimization, and application of sets of logical rules is described. Neural networks are used for initial rule extraction, local or global minimization procedures for optimization, and Gaussian uncertainties of measurements are assumed during application of logical rules. Algorithms for extraction of logical rules from data with real-valued features require determination of linguistic variables or membership functions. Contest-dependent membership functions for crisp and fuzzy linguistic variables are introduced and methods of their determination described. Several neural and machine learning methods of logical rule extraction generating initial rules are described, based on constrained multilayer perceptron, networks with localized transfer functions or on separability criteria for determination of linguistic variables. A tradeoff between accuracy/simplicity is explored at the rule extraction stage and between rejection/error level at the optimization stage. Gaussian uncertainties of measurements are assumed during application of crisp logical rules, leading to "soft trapezoidal" membership functions and allowing to optimize the linguistic variables using gradient procedures. Numerous applications of this methodology to benchmark and real-life problems are reported and very simple crisp logical rules for many datasets provided. PMID:18244384
On the stability of interval type-2 TSK fuzzy logic control systems.
Biglarbegian, Mohammad; Melek, William W; Mendel, Jerry M
Type-2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainties. This paper proposes a novel inference mechanism for an interval type-2 Takagi-Sugeno-Kang fuzzy logic control system (IT2 TSK FLCS) when antecedents are type-2 fuzzy sets and consequents are crisp numbers (A2-C0). The proposed inference mechanism has a closed form which makes it more feasible to analyze the stability of this FLCS. This paper focuses on control applications for the following cases: 1) Both plant and controller use A2-C0 TSK models, and 2) the plant uses type-1 Takagi-Sugeno (TS) and the controller uses IT2 TS models. In both cases, sufficient stability conditions for the stability of the closed-loop system are derived. Furthermore, novel linear-matrix-inequality-based algorithms are developed for satisfying the stability conditions. Numerical analyses are included which validate the effectiveness of the new inference methods. Case studies reveal that an IT2 TS FLCS using the proposed inference engine clearly outperforms its type-1 TSK counterpart. Moreover, due to the simple nature of the proposed inference engine, it is easy to implement in real-time control systems. The methods presented in this paper lay the mathematical foundations for analyzing the stability and facilitating the design of stabilizing controllers of IT2 TSK FLCSs and IT2 TS FLCSs with significantly improved performance over type-1 approaches. PMID:19884090
Fuzzy logic model to describe anesthetic effect and muscular influence on EEG Cerebral State Index.
Brás, S; Gouveia, S; Ribeiro, L; Ferreira, D A; Antunes, L; Nunes, C S
The well-known Cerebral State Index (CSI) quantifies depth of anesthesia and is traditionally modeled with Hill equation and propofol effect-site concentration (Ce). This work brings out two novelties: introduction of electromyogram (EMG) and use of fuzzy logic models with ANFIS optimized parameters. The data were collected from dogs (n=27) during routine surgery considering two propofol administration protocols: constant infusion (G1, n=14) and bolus (G2, n=13). The median modeling error of the fuzzy logic model with Ce and EMG was lower or similar than that of the Hill with Ce (p=0.012-G1, p=0.522-G2). Furthermore, there was no significant performance impact due to model structure alteration (p=0.288-G1, p=0.330-G2) and EMG introduction increased or maintained the performance (p=0.036-G1, p=0.798-G2). Therefore, the new model can achieve higher performance than Hill model, mostly due to EMG information and not due to changes in the model structure. In conclusion, the fuzzy models adequately describe CSI data with advantages over traditional Hill models. PMID:23352353
Fuzzy-logic-based network for complex systems risk assessment: application to ship performance analysis.
Abou, Seraphin C
In this paper, a new interpretation of intuitionistic fuzzy sets in the advanced framework of the Dempster-Shafer theory of evidence is extended to monitor safety-critical systems' performance. Not only is the proposed approach more effective, but it also takes into account the fuzzy rules that deal with imperfect knowledge/information and, therefore, is different from the classical Takagi-Sugeno fuzzy system, which assumes that the rule (the knowledge) is perfect. We provide an analytical solution to the practical and important problem of the conceptual probabilistic approach for formal ship safety assessment using the fuzzy set theory that involves uncertainties associated with the reliability input data. Thus, the overall safety of the ship engine is investigated as an object of risk analysis using the fuzzy mapping structure, which considers uncertainty and partial truth in the input-output mapping. The proposed method integrates direct evidence of the frame of discernment and is demonstrated through references to examples where fuzzy set models are informative. These simple applications illustrate how to assess the conflict of sensor information fusion for a sufficient cooling power system of vessels under extreme operation conditions. It was found that propulsion engine safety systems are not only a function of many environmental and operation profiles but are also dynamic and complex. PMID:22269514
Fuzzy Logic Trajectory Design and Guidance for Terminal Area Energy Management
NASA Technical Reports Server (NTRS)
Burchett, Bradley
The second generation reusable launch vehicle will leverage many new technologies to make flight to low earth orbit safer and more cost effective. One important capability will be completely autonomous flight during reentry and landing, thus making it unnecessary to man the vehicle for cargo missions with stringent weight constraints. Implementation of sophisticated new guidance and control methods will enable the vehicle to return to earth under less than favorable conditions. The return to earth consists of three phases--Entry, Terminal Area Energy Management (TAEM), and Approach and Landing. The Space Shuttle is programmed to fly all three phases of flight automatically, and under normal circumstances the astronaut-pilot takes manual control only during the Approach and Landing phase. The automatic control algorithms used in the Shuttle for TAEM and Approach and Landing have been developed over the past 30 years. They are computationally efficient, and based on careful study of the spacecraft's flight dynamics, and heuristic reasoning. The gliding return trajectory is planned prior to the mission, and only minor adjustments are made during flight for perturbations in the vehicle energy state. With the advent of the X-33 and X-34 technology demonstration vehicles, several authors investigated implementing advanced control methods to provide autonomous real-time design of gliding return trajectories thus enhancing the ability of the vehicle to adjust to unusual energy states. The bulk of work published to date deals primarily with the approach and landing phase of flight where changes in heading angle are small, and range to the runway is monotonically decreasing. These benign flight conditions allow for model simplification and fairly straightforward optimization. This project focuses on the TAEM phase of flight where mathematically precise methods have produced limited results. Fuzzy Logic methods are used to make onboard autonomous gliding return trajectory
Fuzzy Logic Determination of Lithologies from Well Log Data: Application to the KTB Project Data set (Germany)
NASA Astrophysics Data System (ADS)
Bosch, David; Ledo, Juanjo; Queralt, Pilar
Fuzzy logic has been used for lithology prediction with remarkable success. Several techniques such as fuzzy clustering or linguistic reasoning have proven to be useful for lithofacies determination. In this paper, a fuzzy inference methodology has been implemented as a MATLAB routine and applied for the first time to well log data from the German Continental Deep Drilling Program (KTB). The training of the fuzzy inference system is based on the analysis of the multi-class Matthews correlation coefficient computed for the classification matrix. For this particular data set, we have found that the best suited membership function type is the piecewise linear interpolation of the normalized histograms; that the best combination operator for obtaining the final lithology degrees of membership is the fuzzy gamma operator; and that all the available properties are relevant in the classification process. Results show that this fuzzy logic-based method is suited for rapidly and reasonably suggesting a lithology column from well log data, neatly identifying the main units and in some cases refining the classification, which can lead to a better interpretation. We have tested the trained system with synthetic data generated from property value distributions of the training data set to find that the differences in data distributions between both wells are significant enough to misdirect the inference process. However, a cross-validation analysis has revealed that, even with differences between data distributions and missing lithologies in the training data set, this fuzzy logic inference system is able to output a coherent classification.
Minimal sensor count approach to fuzzy logic rotary blood pump flow control.
Casas, Fernando; Ahmed, Nisar; Reeves, Andrew
A rotary blood pump fuzzy logic flow controller without flow sensors was developed and tested in vitro. The controller, implemented in LabView, was set to maintain a flow set point in the presence of external pressure disturbances. Flow was estimated as a function of measured pump's delta P and speed, using a steady-state, nonlinear approximation. The fuzzy controller used the pump's flow estimate and delta P as feedback variables. The defuzzified control output manipulated the pump speed. Membership functions included flow error, delta P, and pump speed. Experimental runs in a mock loop (water/glycerin 3.5 cPs, 37 degrees C), using the estimated flow, were compared with those using a Transonic flow meter for nine conditions of flow and delta P (4 to 6 L/min, 150 to 350 mm Hg). Pressure disturbances generated by a servo pinch valve ranged from +/-23 to +/-47 mm Hg. Results indicated that the fuzzy controller ably regulated the flow set point to within +/-10% of the baseline even under large swings in pressure. There was no difference in controller performance between the ultrasonic flow measurement and the estimated flow calculation scenarios. These tests demonstrated that the fuzzy controller is capable of rejecting disturbances and regulating flow to acceptable limits while using a flow estimate. PMID:17413551
Force control of a tri-layer conducting polymer actuator using optimized fuzzy logic control
NASA Astrophysics Data System (ADS)
Itik, Mehmet; Sabetghadam, Mohammadreza; Alici, Gursel
Conducting polymers actuators (CPAs) are potential candidates for replacing conventional actuators in various fields, such as robotics and biomedical engineering, due to their advantageous properties, which includes their low cost, light weight, low actuation voltage and biocompatibility. As these actuators are very suitable for use in micro-nano manipulation and in injection devices in which the magnitude of the force applied to the target is of crucial importance, the force generated by CPAs needs to be accurately controlled. In this paper, a fuzzy logic (FL) controller with a Mamdani inference system is designed to control the blocking force of a trilayer CPA with polypyrrole electrodes, which operates in air. The particle swarm optimization (PSO) method is employed to optimize the controller's membership function parameters and therefore enhance the performance of the FL controller. An adaptive neuro-fuzzy inference system model, which can capture the nonlinear dynamics of the actuator, is utilized in the optimization process. The optimized Mamdani FL controller is then implemented on the CPA experimentally, and its performance is compared with a non-optimized fuzzy controller as well as with those obtained from a conventional PID controller. The results presented indicate that the blocking force at the tip of the CPA can be effectively controlled by the optimized FL controller, which shows excellent transient and steady state characteristics but increases the control voltage compared to the non-optimized fuzzy controllers.
A self-organizing and self-tuning fuzzy logic controller for field oriented control of induction motor drives
Ashrafzadeh, F.; Nowicki, E.P.; Salmon, J.C.
This paper proposes a design approach for a self-organizing self-tuning fuzzy logic controller, and is applied to the design of a field oriented drive system. The basic structure of a fuzzy logic controller is outlined and the design problems associated with the conventional trial-and-error schemes are addressed. The suitability of the genetic algorithm optimization technique as a means to determine and optimize the fuzzy logic controller design is discussed. In the proposed approach normalization factors and/or membership function parameters and/or the controller policy, are translated into bit-strings. These bit-strings are processed by the genetic algorithm and if the selection process as well as the objective function are chosen properly, a near-optimal solution can be found. To examine the efficiency of the proposed approach, a self-tuning and self-organizing fuzzy logic controller for an indirect field oriented induction motor drive is designed in both a sequential and a concurrent manner. A particular objective function (i.e., a performance index) is chosen to achieve a high dynamic performance. The simulation results demonstrate a significant enhancement in shortening the development time, and improving system performance over a manually tuned fuzzy logic controller.
Fuzzy logic to improve efficiency of finite element and finite difference schemes
Garcia, M.D.; Heger, A.S.
This paper explores possible applications of logic in the areas of finite element and finite difference methods applied to engineering design problems. The application of fuzzy logic to both front-end selection of computational options and within the numerical computation itself are proposed. Further, possible methods of overcoming these limitations through the application of methods are explored. Decision strategy is a fundamental limitation in performing finite element calculations, such as selecting the optimum coarseness of the grid, numerical integration algorithm, element type, implicit versus explicit schemes, and the like. This is particularly true of novice analysts who are confronted with a myriad of choices in performing a calculation. The advantage of having the myriad of options available to the analyst is, however, that it improves and optimizes the design process if the appropriate ones are selected. Unfortunately, the optimum choices are not always apparent and only through the process of elimination or prior extensive experience can the optimum choices or combination of choices be selected. The knowledge of expert analysts could be integrated into a fuzzy ``front-end`` rule-based package to optimize the design process. The use of logic to capture the heuristic and human knowledge for selecting optimum solution strategies sets the framework for these proposed strategies.
Applying fuzzy logic to estimate the parameters of the length-weight relationship.
Bitar, S D; Campos, C P; Freitas, C E C
We evaluated three mathematical procedures to estimate the parameters of the relationship between weight and length for Cichla monoculus: least squares ordinary regression on log-transformed data, non-linear estimation using raw data and a mix of multivariate analysis and fuzzy logic. Our goal was to find an alternative approach that considers the uncertainties inherent to this biological model. We found that non-linear estimation generated more consistent estimates than least squares regression. Our results also indicate that it is possible to find consistent estimates of the parameters directly from the centers of mass of each cluster. However, the most important result is the intervals obtained with the fuzzy inference system. PMID:27143051
GIS modeling using fuzzy logic approach in mineral prospecting based on geophysical data
NASA Astrophysics Data System (ADS)
Setyadi, Harman; Widodo, Lilik Eko; Notosiswoyo, Sudarto; Saptawati, Putri; Ismanto, Arief; Hardjana, Iip
The geophysical exploration method is the superior over the project area due to the dense of vegetation and thick soil so very limited geological outcrops. Contrast of physical properties of every different rock type should be able to be distinguished by the geophysical data. Fuzzy logic approach and weight of evidence were used for geophysical data modeling. Posterior probability was used to calculate the weight of evidence (WofE) of every fuzzy map memberships. By combining each rock type model, the model provides better result compared from the model from mixed rock type on the data training. This method is able to eliminate the potential interference of different geophysical signature. So that, the understanding the geological feature of the area is key success for the mineral prosperity modeling. We verified the model by site visiting and drilling and it is estimated about 90% confident.
Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor
Ondrej Linda; Todd Vollmer; Jason Wright; Milos Manic
Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.
A modular diagnosis system based on fuzzy logic for UASB reactors treating sewage.
Borges, R M; Mattedi, A; Munaro, C J; Franci Gonçalves, R
A modular diagnosis system (MDS), based on the framework of fuzzy logic, is proposed for upflow anaerobic sludge blanket (UASB) reactors treating sewage. In module 1, turbidity and rainfall information are used to estimate the influent organic content. In module 2, a dynamic fuzzy model is used to estimate the current biogas production from on-line measured variables, such as daily average temperature and the previous biogas flow rate, as well as the organic load. Finally, in module 3, all the information above and the residual value between the measured and estimated biogas production are used to provide diagnostic information about the operation status of the plant. The MDS was validated through its application to two pilot UASB reactors and the results showed that the tool can provide useful diagnoses to avoid plant failures. PMID:27438234
Coordinated fuzzy logic control for series capacitor modules and PSS to enhance stability of power system
Hiyama, T.; Mishiro, M.; Kihara, H.; Ortmeyer, T.H.
This paper presents an application of a fuzzy logic control scheme for switched of electric power systems. Through the signal conditioning of the measured real power flow at the location of the series capacitor modules, the phase/speed state of the electric power system is obtained to determine the number of capacitors energized at the state. The switching rules are quite simple so as not to give heavy computation to the micro-computer utilized for the real time switching control of the series capacitor modules. A multimachine system is used to demonstrate the efficiency of the proposed switching control scheme, and coordination with power system stabilizers is considered.
Fuzzy logic based feedback control system for laser beam pointing stabilization.
Singh, Ranjeet; Patel, Kiran; Govindarajan, J; Kumar, Ajai
This paper reports a fuzzy logic based feedback control system for beam pointing stabilization of a high-power nanosecond Nd:YAG laser operating at 30 Hz. This is achieved by generating the correcting signal for each consequent pulse from the error in the pointing position of the previous laser pulse. We have successfully achieved a reduction of beam position fluctuation from ±60 to ±5.0 μrad without the focusing optics and ±0.9 μrad with focusing optics. PMID:20856289
Fuzzy Logic Based Controller for a Grid-Connected Solid Oxide Fuel Cell Power Plant.
Chatterjee, Kalyan; Shankar, Ravi; Kumar, Amit
This paper describes a mathematical model of a solid oxide fuel cell (SOFC) power plant integrated in a multimachine power system. The utilization factor of a fuel stack maintains steady state by tuning the fuel valve in the fuel processor at a rate proportional to a current drawn from the fuel stack. A suitable fuzzy logic control is used for the overall system, its objective being controlling the current drawn by the power conditioning unit and meet a desirable output power demand. The proposed control scheme is verified through computer simulations. PMID:25053926
Intelligent control of a multi-degree-of freedom reaction compensating platform system using fuzzy logic
NASA Technical Reports Server (NTRS)
Choi, Benjamin B.; Lawrence, Charles; Lin, Yueh-Jaw
This paper presents the development of a general-purpose fuzzy logic (FL) control methodology for isolating the external vibratory disturbances of space-based devices. According to the desired performance specifications, a full investigation regarding the development of an FL controller was done using different scenarios, such as variances of passive reaction-compensating components and external disturbance load. It was shown that the proposed FL controller is robust in that the FL-controlled system closely follows the prespecified ideal reference model. The comparative study also reveals that the FL-controlled system achieves significant improvement in reducing vibrations over passive systems.
Chaotic queue-based genetic algorithm for design of a self-tuning fuzzy logic controller
NASA Astrophysics Data System (ADS)
Saini, Sanju; Saini, J. S.
This paper employs a chaotic queue-based method using logistic equation in a non-canonical genetic algorithm for optimizing the performance of a self-tuning Fuzzy Logic Controller, used for controlling a nonlinear double-coupled system. A comparison has been made with a standard canonical genetic algorithm implemented on the same plant. It has been shown that chaotic queue-method brings an improvement in the performance of the FLC for wide range of set point changes by a more profound initial population spread in the search space.
Quantitative high resolution electron microscopy of III-V compounds: A fuzzy logic approach
NASA Astrophysics Data System (ADS)
Hillebrand, R.; Hofmeister, H.; Werner, P.; Gösele, U.
In the study of interdiffusion phenomena in layered structures of III-V compounds by high resolution electron microscopy, contrast features in the micrographs can be correlated with the variation of the chemical composition of the crystals. For quantitative interpretation of the micrographs a fuzzy logic approach is adapted to extract chemical information. The linguistic variable ``similarity of images'' is derived from the standard deviation (SD) of their difference patterns, which proved to be an appropriate measure. The approach developed is used to analyze simulated contrast tableaus of GaAs/P (As/P variation) and experimental micrographs of Al/GaAs (Al/Ga variation).
Fuzzy logic enhanced speed control of an indirect field-oriented induction machine drive
Heber, B.; Xu, L.; Tang, Y.
Field orientation control (FOC) of induction machines has permitted fast transient response by decoupled torque and flux control. However, field orientation detuning caused by parameter variations is a major difficulty for indirect FOC methods. Traditional probability density function (PID) controllers have trouble meeting a wide range of speed tracking performance even when proper field orientation is achieved. PID controller performance is severely degraded when detuning occurs. This paper presents a fuzzy logic design approach that can meet the speed tracking requirements even when detuning occurs. Computer simulations and experimental results obtained via a general-purpose digital signal processor (DSP) system are presented.
Fuzzy logic application for modeling man-in-the-loop space shuttle proximity operations. M.S. Thesis - MIT
NASA Technical Reports Server (NTRS)
Brown, Robert B.
A software pilot model for Space Shuttle proximity operations is developed, utilizing fuzzy logic. The model is designed to emulate a human pilot during the terminal phase of a Space Shuttle approach to the Space Station. The model uses the same sensory information available to a human pilot and is based upon existing piloting rules and techniques determined from analysis of human pilot performance. Such a model is needed to generate numerous rendezvous simulations to various Space Station assembly stages for analysis of current NASA procedures and plume impingement loads on the Space Station. The advantages of a fuzzy logic pilot model are demonstrated by comparing its performance with NASA's man-in-the-loop simulations and with a similar model based upon traditional Boolean logic. The fuzzy model is shown to respond well from a number of initial conditions, with results typical of an average human. In addition, the ability to model different individual piloting techniques and new piloting rules is demonstrated.
Development of a fuzzy logic based intelligent system for autonomous guidance of post-stroke rehabilitation exercise.
Huq, Rajibul; Wang, Rosalie; Lu, Elaine; Hebert, Debbie; Lacheray, Hervé; Mihailidis, Alex
This paper presents preliminary studies in developing a fuzzy logic based intelligent system for autonomous post-stroke upper-limb rehabilitation exercise. The intelligent system autonomously varies control parameters to generate different haptic effects on the robotic device. The robotic device is able to apply both resistive and assistive forces for guiding the patient during the exercise. The fuzzy logic based decision-making system estimates muscle fatigue of the patient using exercise performance and generates a combination of resistive and assistive forces so that the stroke survivor can exercise for longer durations with increasing control. The fuzzy logic based system is initially developed using a study with healthy subjects and preliminary results are also presented to validate the developed system with healthy subjects. The next stage of this work will collect data from stroke survivors for further development of the system. PMID:24187289
Fuzzy logic motor control for pollution prevention and improved energy efficiency. Report for October 1992-August 1993
Spiegel, R.J.; Chappell, P.J.; Maxwell, M.A.; Cleland, J.G.; Bose, B.K.
The paper discusses an EPA program investigating fuzzy logic motor control for improved pollution prevention and energy efficiency. Initial computer simulation and laboratory results have demonstrated that fuzzy logic energy optimizers can consistently improve motor operational efficiency over conventional adjustable-speed drive (ASD) operation. This is significant in terms of both potential U.S. pollution prevention and energy savings possibilities. The addition of a fuzzy logic energy optimizer microchip to an ASD is minimal in terms of cost because low-power microchips are very cheap when manufactured in volume. Thus, the energy savings and enhanced motor operation and lifespan should be regarded as almost free because cost payback is potentially so short.
Environmental impact assessment by means of a procedure based on fuzzy logic: A practical application
Peche, Roberto Rodriguez, Esther
This study shows the practical application of the EIA method based on fuzzy logic proposed by the authors (Peche and Rodriguez, 2009) to a simplified case of study-the activity of a petrol station throughout its exploitation. The intensity (p{sub 1}), the extent (p{sub 2}) and the persistence (p{sub 3}) were the properties selected to describe the impacts and their respective assessment functions v-bar{sub i}=f(p-bar{sub i}) were determined. The main actions (A) and potentially affected environmental factors (F) were selected. Every impact was identified by a pair A-F and the values of the three impact properties were estimated for each of them by means of triangular fuzzy numbers. Subsequently, the fuzzy estimation of every impact was carried out, the estimation of the impact A{sub 1}-F{sub 2} (V-bar{sub 1}) being explained in detail. Every impact was simultaneously represented by its corresponding generalised confidence interval and membership function. Since the membership functions of all impacts were similar to triangular fuzzy numbers, a triangular approach (TA) was used to describe every impact. A triangular approach coefficient (TAC) was introduced to quantify the similarity of each fuzzy number and its corresponding triangular approach, where TAC (V-bar) element of (0, 1] and TAC being 1 when the fuzzy number is triangular. The TACs-ranging from 0.96 to 0.99-proved that TAs were valid in all cases. Next, the total positive and negative impacts-TV-bar{sup +} and TV-bar{sup -} were calculated and later, the fuzzy value of the total environmental impact TV-bar was determined from them. Finally, the defuzzification of TV-bar led to the punctual impact estimator TV{sup (1)} = -88.50 and its corresponding uncertainty interval [{delta}{sub l}(TV-bar),{delta}{sub r}(TV-bar)]=[6.52,6.96], which represent the total value of the EI. In conclusion, the EIA method enabled the integration of heterogeneous impacts, which exerted influence on environmental factors of a
Contrast of evolution models for agricultural contaminants in ground waters by means of fuzzy logic and data mining
NASA Astrophysics Data System (ADS)
Andujar, J. M.; Aroba, J.; de Torre, M. L. La; Grande, J. A.
This work aims at contrasting, by means of a set of fuzzy logic- and data mining-based algorithms, the functioning model of a detritic aquifer undergoing overexploitation and nitrate excess input coming from strawberry and citrus intensive crops in its recharge zone. To provide researchers unskilled in data mining techniques with an easy and intuitive interpretation, the authors have developed a computer tool based on fuzzy logic that allows immediate qualitative analysis of the data contained in a data mass from the water chemical analyses, and serves as a contrast to functioning models previously proposed with classical statistics.
Interference Path Loss Prediction in A319/320 Airplanes Using Modulated Fuzzy Logic and Neural Networks
NASA Technical Reports Server (NTRS)
Jafri, Madiha J.; Ely, Jay J.; Vahala, Linda L.
In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.
Fuzzy logic based sensor performance evaluation of vehicle mounted metal detector systems
NASA Astrophysics Data System (ADS)
Abeynayake, Canicious; Tran, Minh D.
Vehicle Mounted Metal Detector (VMMD) systems are widely used for detection of threat objects in humanitarian demining and military route clearance scenarios. Due to the diverse nature of such operational conditions, operational use of VMMD without a proper understanding of its capability boundaries may lead to heavy causalities. Multi-criteria fitness evaluations are crucial for determining capability boundaries of any sensor-based demining equipment. Evaluation of sensor based military equipment is a multi-disciplinary topic combining the efforts of researchers, operators, managers and commanders having different professional backgrounds and knowledge profiles. Information acquired through field tests usually involves uncertainty, vagueness and imprecision due to variations in test and evaluation conditions during a single test or series of tests. This report presents a fuzzy logic based methodology for experimental data analysis and performance evaluation of VMMD. This data evaluation methodology has been developed to evaluate sensor performance by consolidating expert knowledge with experimental data. A case study is presented by implementing the proposed data analysis framework in a VMMD evaluation scenario. The results of this analysis confirm accuracy, practicability and reliability of the fuzzy logic based sensor performance evaluation framework.
Direction-of-arrival interferometer array design using genetic algorithm with fuzzy logic
NASA Astrophysics Data System (ADS)
Straatveit, S. Nils
Design of interferometer arrays for radio frequency direction of arrival estimation involves optimizing conflicting requirements. For example, high resolution conflicts with low cost. Lower level requirements also invoke lower level design issues such as ambiguity in direction of arrival angle. A more efficient array design process is described here, which uses a genetic algorithm with a growing genome and fuzzy logic scoring. Extensive simulation software is also needed. Simulation starts with randomized small array configurations. These are then evaluated against the fitness functions with results scored using fuzzy logic. The best-fit of the population are combined to produce the next generation. A mutation function introduces slight randomness in some genomes. Finally, if the overall population scores well the size of the genome is increased until final genome size is consistent with the desired array resolution requirement. The genetic algorithm design process described here produced a number of array designs. The results indicate discrete stages or steps in the optimization and an interesting trade-off of lower resolution for greater accuracy.
Application of fuzzy logic to optimize placement of an acquisition, tracking, and pointing experiment
NASA Astrophysics Data System (ADS)
Bukley, Jerry
The experiment is comprised of a 115,000 cubic meter helium balloon which lifts a 2,900 kg Acquisition, Tracking and Pointing (ATP) experiment package to an altitude of 26 km. The Phillips Laboratory High Altitude Balloon Experiment (HABE) has been developed as a cost-effective means of testing satellite ATP technologies in an environment similar to space. A major advantage of the concept is the flexibility in placement and timing afforded a balloon over a satellite. This flexibility allows HABE to engage targets-of-opportunity launched from the domestic ranges without requiring a dedicated or closely coordinated launch time. The placement of HABE is optimized to maximize active track time. A routine was developed to raster scan the mathematical model of a flight corridor while accumulating the intervals of continuous engagement that satisfy a list of ten rules. Although successful, this method is unable to place priorities or make trades based on the relative importance of the rules. The use of fuzzy logic in the form of approximate reasoning to evaluate the rules, while also considering goals, enables key qualitative considerations to be factored into the overall evaluation. This paper describes the application of fuzzy logic to data analysis and compares the results to conventional Boolean techniques.
Uncertainty handling in structural damage detection using fuzzy logic and probabilistic simulation
NASA Astrophysics Data System (ADS)
Chandrashekhar, M.; Ganguli, Ranjan
A fuzzy logic system (FLS) with a new sliding window defuzzifier is developed for damage detection. The effect of changes in the damage evaluation parameter (frequency) due to uncertainty in material properties is explored and the results of the probabilistic analysis are used to develop a robust FLS for damage detection. Probabilistic analysis is performed using Monte Carlo Simulation (MCS) on a beam finite element (FE) model to calculate statistical properties of the variation in natural frequencies of the beam due to structural damage and material uncertainty. Variation in these frequency measures, further contaminated with measurement noise, are used for testing the FLS. The FLS developed for damage detection in the steel beam having material uncertainty (elastic modulus) with coefficient of variation (COV) of 3 percent and noise level of 0.15 in the measurement data, correctly identifies the fault with an accuracy of about 94 percent. The FLS also accurately classifies the undamaged condition in presence of the mentioned uncertainties reducing the possibility of false alarms. From an algorithmic standpoint, this paper connects the disparate areas of probability and fuzzy logic to alleviate uncertainty issues in damage detection.
Energy management strategies of a fuel cell/battery hybrid system using fuzzy logics
NASA Astrophysics Data System (ADS)
Jeong, Kwi-Seong; Lee, Won-Yong; Kim, Chang-Soo
Hybrid power systems with fuel cells and batteries have the great potential to improve the operation efficiency and dynamic response. A proper load management strategy is important for both better system efficiency and endurance of hybrid systems. In this paper, a fuzzy logic algorithm has been used to determine the fuel cell output power depending on the external power requirement and the battery state of charge (SoC). If the power requirement of the hybrid system is low and the SoC is low, then the greater part of the fuel cell power is used to charge the battery pack. If the power requirement is relatively high and the SoC is also high, then the fuel cell and the battery are concurrently used to supply the required power. These if-then operation rules are implemented by fuzzy logic for the energy management of the hybrid system. The strategy is evaluated using simulation and experimental results. The results show that the operation efficiency of hybrid system was improved and the battery SoC maintained at reasonable level. The control scheme can be used to optimize the operational efficiency of hybrid power generation system.
Fuzzy logic autopilot synthesis for a nonlinearly behaved thruster-controlled missile
NASA Astrophysics Data System (ADS)
Schroeder, Wayne Kevin
A methodology to design a fuzzy logic based autopilot control system that works with vehicle nonlinearities (not merely accommodating them) is defined. The application is a forward propulsive controlled, nonlinearly behaved missile. The three distinct types of nonlinearities studied are nonlinear aerodynamics, impulsive thruster control, and aerodynamic and propulsive jet interaction. To permit sufficient fidelity of simulation models, an empirical approach to defining the jet interaction characteristics is developed and applied to the study missile configuration. The non-minimum phase trait of jet interaction coupled with the transient effects of impulsive control pose the greatest challenge to the autopilot design. The key innovation for controlling the coupled and destabilizing nonlinear missile behavior is to incorporate dynamic compensation into the inherently static fuzzy logic control structure. The autopilot structure is defined and an efficient path for finding a controller solution is identified. Acceleration step responses which respond more rapidly than the linearized airframe approximations with minimal airframe oscillation validate the achievement of the research goal.
Application of Fuzzy Logic to EMS-type Magnetically Levitated Railway Vehicle
NASA Astrophysics Data System (ADS)
Kusagawa, Shinichi; Baba, Jumpei; Shutoh, Katsuhiko; Masada, Eisuke
A type of the magnetically levitated railway system with the electro-magnetic suspension system (EMS), which is named HSST system, will be put into revenue service as an urban transport in Nagoya, Japan at the beginning of April 2005. To extend its operational velocity higher than 200km/h for applications in other cities, the design of its EMS system is reexamined for improvement of riding comfort and performances of a train. In order to achieve these objectives, the multipurpose optimization on the basis of the genetic algorithm is applied for the design of EMS-type magnetically levitated vehicle, control parameters of which are optimized both to follow the rail exactly in high-speed and to provide enough riding comfort to passengers. However, the ability to follow sharp irregularities of the rail and to cope with high frequency noises in the gap length control system should be coordinated with riding comfort. The fuzzy logic is introduced into the dynamic control loop and verified to solve the problem. Far better coordination is obtained between the vehicle performances and riding comfort of passengers in high-speed against such various rail conditions. The levitation control with fuzzy logic is shown to be useful for the critical design problem as the high-speed maglev railways.
Fuzzy logic control of stand-alone photovoltaic system with battery storage
NASA Astrophysics Data System (ADS)
Lalouni, S.; Rekioua, D.; Rekioua, T.; Matagne, E.
Photovoltaic energy has nowadays an increased importance in electrical power applications, since it is considered as an essentially inexhaustible and broadly available energy resource. However, the output power provided via the photovoltaic conversion process depends on solar irradiation and temperature. Therefore, to maximize the efficiency of the photovoltaic energy system, it is necessary to track the maximum power point of the PV array. The present paper proposes a maximum power point tracker (MPPT) method, based on fuzzy logic controller (FLC), applied to a stand-alone photovoltaic system. It uses a sampling measure of the PV array power and voltage then determines an optimal increment required to have the optimal operating voltage which permits maximum power tracking. This method carries high accuracy around the optimum point when compared to the conventional one. The stand-alone photovoltaic system used in this paper includes two bi-directional DC/DC converters and a lead-acid battery bank to overcome the scare periods. One converter works as an MPP tracker, while the other regulates the batteries state of charge and compensates the power deficit to provide a continuous delivery of energy to the load. The Obtained simulation results show the effectiveness of the proposed fuzzy logic controller.
Application of ANN and fuzzy logic algorithms for streamflow modelling of Savitri catchment
NASA Astrophysics Data System (ADS)
Kothari, Mahesh; Gharde, K. D.
The streamflow prediction is an essentially important aspect of any watershed modelling. The black box models (soft computing techniques) have proven to be an efficient alternative to physical (traditional) methods for simulating streamflow and sediment yield of the catchments. The present study focusses on development of models using ANN and fuzzy logic (FL) algorithm for predicting the streamflow for catchment of Savitri River Basin. The input vector to these models were daily rainfall, mean daily evaporation, mean daily temperature and lag streamflow used. In the present study, 20 years (1992-2011) rainfall and other hydrological data were considered, of which 13 years (1992-2004) was for training and rest 7 years (2005-2011) for validation of the models. The mode performance was evaluated by R, RMSE, EV, CE, and MAD statistical parameters. It was found that, ANN model performance improved with increasing input vectors. The results with fuzzy logic models predict the streamflow with single input as rainfall better in comparison to multiple input vectors. While comparing both ANN and FL algorithms for prediction of streamflow, ANN model performance is quite superior.
Hybrid intelligent control scheme for air heating system using fuzzy logic and genetic algorithm
Thyagarajan, T.; Shanmugam, J.; Ponnavaikko, M.; Panda, R.C.
Fuzzy logic provides a means for converting a linguistic control strategy, based on expert knowledge, into an automatic control strategy. Its performance depends on membership function and rule sets. In the traditional Fuzzy Logic Control (FLC) approach, the optimal membership is formed by trial-and-error method. In this paper, Genetic Algorithm (GA) is applied to generate the optimal membership function of FLC. The membership function thus obtained is utilized in the design of the Hybrid Intelligent Control (HIC) scheme. The investigation is carried out for an Air Heat System (AHS), an important component of drying process. The knowledge of the optimum PID controller designed, is used to develop the traditional FLC scheme. The computational difficulties in finding optimal membership function of traditional FLC is alleviated using GA In the design of HIC scheme. The qualitative performance indices are evaluated for the three control strategies, namely, PID, FLC and HIC. The comparison reveals that the HIC scheme designed based on the hybridization of FLC with GA performs better. Moreover, GA is found to be an effective tool for designing the FLC, eliminating the human interface required to generate the membership functions.
Computer-aided bone age assessment for ethnically diverse older children using integrated fuzzy logic system
NASA Astrophysics Data System (ADS)
Ma, Kevin; Moin, Paymann; Zhang, Aifeng; Liu, Brent
Bone Age Assessment (BAA) of children is a clinical procedure frequently performed in pediatric radiology to evaluate the stage of skeletal maturation based on the left hand x-ray radiograph. The current BAA standard in the US is using the Greulich & Pyle (G&P) Hand Atlas, which was developed fifty years ago and was only based on Caucasian population from the Midwest US. To bring the BAA procedure up-to-date with today's population, a Digital Hand Atlas (DHA) consisting of 1400 hand images of normal children of different ethnicities, age, and gender. Based on the DHA and to solve inter- and intra-observer reading discrepancies, an automatic computer-aided bone age assessment system has been developed and tested in clinical environments. The algorithm utilizes features extracted from three regions of interests: phalanges, carpal, and radius. The features are aggregated into a fuzzy logic system, which outputs the calculated bone age. The previous BAA system only uses features from phalanges and carpal, thus BAA result for children over age of 15 is less accurate. In this project, the new radius features are incorporated into the overall BAA system. The bone age results, calculated from the new fuzzy logic system, are compared against radiologists' readings based on G&P atlas, and exhibits an improvement in reading accuracy for older children.
SVR learning-based spatiotemporal fuzzy logic controller for nonlinear spatially distributed dynamic systems.
Zhang, Xian-Xia; Jiang, Ye; Li, Han-Xiong; Li, Shao-Yuan
A data-driven 3-D fuzzy-logic controller (3-D FLC) design methodology based on support vector regression (SVR) learning is developed for nonlinear spatially distributed dynamic systems. Initially, the spatial information expression and processing as well as the fuzzy linguistic expression and rule inference of a 3-D FLC are integrated into spatial fuzzy basis functions (SFBFs), and then the 3-D FLC can be depicted by a three-layer network structure. By relating SFBFs of the 3-D FLC directly to spatial kernel functions of an SVR, an equivalence relationship of the 3-D FLC and the SVR is established, which means that the 3-D FLC can be designed with the help of the SVR learning. Subsequently, for an easy implementation, a systematic SVR learning-based 3-D FLC design scheme is formulated. In addition, the universal approximation capability of the proposed 3-D FLC is presented. Finally, the control of a nonlinear catalytic packed-bed reactor is considered as an application to demonstrate the effectiveness of the proposed 3-D FLC. PMID:24808600
Fuzzy logic applied to shaped charge jet penetration of glass composite
NASA Astrophysics Data System (ADS)
Pritchard, Dennis S.
A shaped charge warhead, designed to produce an ideal jet, always has at least some small manufacturing flaws that degrade jet formation. This degradation is reflected in a vague or fuzzy sense when its effect in turn degrades penetration performance. Fuzzy logic was used to relate warhead performance to manufacturing errors. Fuzzy penetration versus standoff points are predicted to lie within a confidence band with boundaries perpendicular to a linear least-squares fit (LLSQ) to 13 well-distributed experimental data points for a shaped charge penetrating semi-infinite S2 Glass composite. On a penetration versus standoff plot, the LLSQ line at zero standoff intercepts the penetration axis at 8.01 charge diameters (CD), and at 20.00 CD standoff (the outer experimental limit) the penetration is 2.89 CD. For any standoff over the experimental range, predicted penetration is within ±11% of the least-squares value at that standoff. This percentage interval forms the upper and lower boundary for the confidence band.
A rule-based fuzzy logic controller for a PWM inverter in a stand alone wind energy conversion scheme
Hilloowala, R.M.; Sharaf, A.M.
The paper presents a rule-based fuzzy logic controller to control the output power of a pulse width modulated (PWM) inverter used in a stand alone wind energy conversion scheme (SAWECS). The self-excited induction generator used in SAWECS has the inherent problem of fluctuations in the magnitude and frequency of its terminal voltage with changes in wind velocity and load. To overcome this drawback the variable magnitude, variable frequency voltage at the generator terminals is rectified and the dc power is transferred to the load through a PWM inverter. The objective is to track and extract maximum power from the wind energy system (WES) and transfer this power to the local isolated load. This is achieved by using the fuzzy logic controller which regulates the modulation index of the PWM inverter based on the input signals: the power error e = (P{sub ref} {minus} P{sub o}) and its rate of change {dot e}. These input signals are fuzzified, that is defined by a set of linguistic labels characterized by their membership functions predefined for each class. Using a set of 49 rules which relate the fuzzified input signals (e, {dot e}) to the fuzzy controller output U, fuzzy set theory and associated fuzzy logic operations, the fuzzy controller`s output (in terms of linguistic labels) is defuzzified to obtain the actual analog (numerical) output signal which is then used to control the PWM inverter and ensure complete utilization of the available wind energy. The proposed rule-based fuzzy logic controller is simulated and the results are experimentally verified on a scaled down laboratory prototype of the SAWECS.
A comparative study of fuzzy logic systems approach for river discharge prediction
NASA Astrophysics Data System (ADS)
Jayawardena, A. W.; Perera, E. D. P.; Zhu, Bing; Amarasekara, J. D.; Vereivalu, V.
In recent years, flood disasters resulting from extreme rainfall have been on the increase in many regions of the world. In developed countries, the usual practice of mitigating flood disasters is by structural means which can reduce infrastructural damages as well as casualties but are unaffordable in most developing countries. The alternative then is to look for non-structural means that involve, among other things, early warning systems which can reduce casualties. The basic technical components of an early warning system involves a measurable input data set that trigger floods, a measurable output data set that quantify the extent of flood and an appropriate mathematical model that transforms the input data set into a corresponding output data set. There are many types of mathematical models that can be used to transform the input data into corresponding output data. The crux of this paper is on one type of data driven mathematical models, namely the use of fuzzy logic approach. The reliability and robustness of the approach are demonstrated with daily and 6-hourly discharge predictions in 4 rivers in 3 countries having contrasting climatological, geographical and land use characteristics. The first application is for two tropical rivers in Sri Lanka using daily upstream rainfall and discharge data to predict downstream discharge with the minimum implication function type Mamdani fuzzy inference system. The second application is for another tropical river in Fiji using similar type of data with daily and 6-h time scales. Both Mamdani type fuzzy inference system with minimum and product implication functions as well as Larsen type inference systems were used. In the third application, daily upstream and tributary discharges were used to predict downstream discharges in a temperate-climate river in China using the TSK type fuzzy inference system with clustering. The methods are robust and the results obtained are within reasonable agreement with observations.
Fuzzy Logic Classification of Imaging Laser Desorption Fourier Transform Mass Spectrometry Data
Timothy R. McJunkin; Jill R. Scott
The fuzzy logic method is applied to classification of mass spectra obtained with an imaging internal source Fourier transform mass spectrometer (I2LD-FTMS). Traditionally, an operator uses the relative abundance of ions with specific mass-to-charge (m/z) ratios to categorize spectra. An operator does this by comparing the spectrum of m/z versus abundance of an unknown sample against a library of spectra from known samples. Automated positioning and acquisition allow the I2LD-FTMS to acquire data from very large grids, which would require classification of up to 3600 spectra per hour to keep pace with the acquisition. The tedious job of classifying numerous spectra generated in an I2LD-FTMS imaging application can be replaced by a fuzzy rule base if the cues an operator uses can be encapsulated. Appropriate methods for assigning fuzzy membership values for inputs (e.g., mass spectrum abundances) and choice of fuzzy inference operators to translate linguistic antecedent into confidence values for the consequence (or in this case the classification) is followed by using the maximum confidence and a necessary minimum threshold for making a crisp decision. This paper also describes a method for gathering statistics on ions, which are not currently used in the rule base, but which may be candidates for making the rule base more accurate and complete or to form new rule bases based on data obtained from known samples. A spatial method for classifying spectra with low membership values, based on neighboring sample classifications, is also presented.
Dynamic Fuzzy Logic-Based Quality of Interaction within Blended-Learning: The Rare and Contemporary Dance Cases
ERIC Educational Resources Information Center
Dias, Sofia B.; Diniz, José A.; Hadjileontiadis, Leontios J.
The combination of the process of pedagogical planning within the Blended (b-) learning environment with the users' quality of interaction ("QoI") with the Learning Management System (LMS) is explored here. The required "QoI" (both for professors and students) is estimated by adopting a fuzzy logic-based modeling approach,…
Extending human perception of electromagnetic radiation to the UV region through biologically inspired photochromic fuzzy logic (BIPFUL) systems.
Gentili, Pier Luigi; Rightler, Amanda L; Heron, B Mark; Gabbutt, Christopher D
Photochromic fuzzy logic systems have been designed that extend human visual perception into the UV region. The systems are founded on a detailed knowledge of the activation wavelengths and quantum yields of a series of thermally reversible photochromic compounds. By appropriate matching of the photochromic behaviour unique colour signatures are generated in response differing UV activation frequencies. PMID:26658700
Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems
ERIC Educational Resources Information Center
Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.
Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…
Taming Data to Make Decisions: Using a Spatial Fuzzy Logic Decision Support Framework to Inform Conservation and Land Use Planning
NASA Astrophysics Data System (ADS)
Sheehan, T.; Baker, B.; Degagne, R. S.
With the abundance of data sources, analytical methods, and computer models, land managers are faced with the overwhelming task of making sense of a profusion of data of wildly different types. Luckily, fuzzy logic provides a method to work with different types of data using language-based propositions such as "the landscape is undisturbed," and a simple set of logic constructs. Just as many surveys allow different levels of agreement with a proposition, fuzzy logic allows values reflecting different levels of truth for a proposition. Truth levels fall within a continuum ranging from Fully True to Fully False. Hence a fuzzy logic model produces continuous results. The Environmental Evaluation Modeling System (EEMS) is a platform-independent, tree-based, fuzzy logic modeling framework. An EEMS model provides a transparent definition of an evaluation model and is commonly developed as a collaborative effort among managers, scientists, and GIS experts. Managers specify a set of evaluative propositions used to characterize the landscape. Scientists, working with managers, formulate functions that convert raw data values into truth values for the propositions and produce a logic tree to combine results into a single metric used to guide decisions. Managers, scientists, and GIS experts then work together to implement and iteratively tune the logic model and produce final results. We present examples of two successful EEMS projects that provided managers with map-based results suitable for guiding decisions: sensitivity and climate change exposure in Utah and the Colorado Plateau modeled for the Bureau of Land Management; and terrestrial ecological intactness in the Mojave and Sonoran region of southern California modeled for the Desert Renewable Energy Conservation Plan.
A Sliding Mode Controller Using Nonlinear Sliding Surface Improved With Fuzzy Logic: Application to the Coupled Tanks System
NASA Astrophysics Data System (ADS)
Boubakir, A.; Boudjema, F.; Boubakir, C.
This paper proposes an approach of hybrid control that is based on the concept of combining fuzzy logic and the methodology of sliding mode control (SMC). In the present works, a first-order nonlinear sliding surface is presented, on which the developed control law is based. Mathematical proof for the stability and convergence of the system is presented. In order to reduce the chattering in sliding mode control, a fixed boundary layer around the switch surface is used. Within the boundary layer, since the fuzzy logic control is applied, the chattering phenomenon, which is inherent in a sliding mode control, is avoided by smoothing the switch signal. Outside the boundary, the sliding mode control is applied to driving the system states into the boundary layer. Experimental studies carried out on a coupled Tanks system indicate that the proposed fuzzy sliding mode control (FSMC) is a good candidate for control applications.
A manufacturing quality assessment model based-on two stages interval type-2 fuzzy logic
NASA Astrophysics Data System (ADS)
Purnomo, Muhammad Ridwan Andi; Helmi Shintya Dewi, Intan
This paper presents the development of an assessment models for manufacturing quality using Interval Type-2 Fuzzy Logic (IT2-FL). The proposed model is developed based on one of building block in sustainable supply chain management (SSCM), which is benefit of SCM, and focuses more on quality. The proposed model can be used to predict the quality level of production chain in a company. The quality of production will affect to the quality of product. Practically, quality of production is unique for every type of production system. Hence, experts opinion will play major role in developing the assessment model. The model will become more complicated when the data contains ambiguity and uncertainty. In this study, IT2-FL is used to model the ambiguity and uncertainty. A case study taken from a company in Yogyakarta shows that the proposed manufacturing quality assessment model can work well in determining the quality level of production.
Fuzzy-logic-based approach to study the electrons mobility in nanoscale Double Gate MOSFETs
NASA Astrophysics Data System (ADS)
Bendib, T.; Djeffal, F.; Arar, D.; Dibi, Z.; Ferdi, A.
The Double Gate silicon (DG) MOSFET with extremely short-channel length has the appropriate features to constitute the devices for nanoscale circuit design. To develop a physical model for extremely scaled DG MOSFETs, the electron mobility distribution throughout the channel must be accurately determined under the application of drain and gate voltages. However, the modeling of the transport behavior for the nanoscale structures requires the use of overkill methods and models in terms of complexity and computation time (self-consistent, quantum computations,..). Therefore, new methods and techniques are required to overcome these constraints. In this work, a new approach based on the fuzzy logic computation is proposed to model the electron mobility behavior for nanoscale DG MOSFETs. The proposed approach can be implemented into devices simulator to study the nanoscale CMOS-based circuits.
Fuzzy Logic as a Computational Tool for Quantitative Modelling of Biological Systems with Uncertain Kinetic Data.
Bordon, Jure; Moskon, Miha; Zimic, Nikolaj; Mraz, Miha
Quantitative modelling of biological systems has become an indispensable computational approach in the design of novel and analysis of existing biological systems. However, kinetic data that describe the system's dynamics need to be known in order to obtain relevant results with the conventional modelling techniques. These data are often hard or even impossible to obtain. Here, we present a quantitative fuzzy logic modelling approach that is able to cope with unknown kinetic data and thus produce relevant results even though kinetic data are incomplete or only vaguely defined. Moreover, the approach can be used in the combination with the existing state-of-the-art quantitative modelling techniques only in certain parts of the system, i.e., where kinetic data are missing. The case study of the approach proposed here is performed on the model of three-gene repressilator. PMID:26451831
Non-Linear Fuzzy Logic Control for Forced Large Motions of Spinning Shafts
NASA Astrophysics Data System (ADS)
A unique control approach is developed for prescribed large motion control using magnetic bearings in a proposed active stall control test rig. A finite element based, flexible shaft is modeled in a closed loop system with PD controllers that generate the control signals to support and to shake the rotor shaft. A linearized force model of the stall rig with 16 magnetic poles (4 opposing C-cores) yields stability and frequency responses. The non-linear model retains the non-linearities in Ampere's law, Faraday's law and the Maxwell stress tensor. A fuzzy logic control system is then designed to show the advantages over the conventional controllers with the fully non-linear model.
Application of fuzzy logic to the control of wind tunnel settling chamber temperature
NASA Technical Reports Server (NTRS)
Gwaltney, David A.; Humphreys, Gregory L.
The application of Fuzzy Logic Controllers (FLC's) to the control of nonlinear processes, typically controlled by a human operator, is a topic of much study. Recent application of a microprocessor-based FLC to the control of temperature processes in several wind tunnels has proven to be very successful. The control of temperature processes in the wind tunnels requires the ability to monitor temperature feedback from several points and to accommodate varying operating conditions in the wind tunnels. The FLC has an intuitive and easily configurable structure which incorporates the flexibility required to have such an ability. The design and implementation of the FLC is presented along with process data from the wind tunnels under automatic control.
Smart greenhouse fuzzy logic based control system enhanced with wireless data monitoring.
Azaza, M; Tanougast, C; Fabrizio, E; Mami, A
Greenhouse climate control is complicated procedure since the number of variables involved on it and which are dependent on each other. This paper presents a contribution to integrate greenhouse inside climate key's parameters, leading to promote a comfortable micro-climate for the plants growth while saving energy and water resources. A smart fuzzy logic based control system was introduced and improved through specific measure to the temperature and humidity correlation. As well, the system control was enhanced with wireless data monitoring platform for data routing and logging, which provides real time data access. The proposed control system was experimentally validated. The efficiency of the system was evaluated showing important energy and water saving. PMID:26749556
Fuzzy logic based on-line fault detection and classification in transmission line.
Adhikari, Shuma; Sinha, Nidul; Dorendrajit, Thingam
This study presents fuzzy logic based online fault detection and classification of transmission line using Programmable Automation and Control technology based National Instrument Compact Reconfigurable i/o (CRIO) devices. The LabVIEW software combined with CRIO can perform real time data acquisition of transmission line. When fault occurs in the system current waveforms are distorted due to transients and their pattern changes according to the type of fault in the system. The three phase alternating current, zero sequence and positive sequence current data generated by LabVIEW through CRIO-9067 are processed directly for relaying. The result shows that proposed technique is capable of right tripping action and classification of type of fault at high speed therefore can be employed in practical application. PMID:27398278
[Research on the Application of Fuzzy Logic to Systems Analysis and Control
NASA Technical Reports Server (NTRS)
Research conducted with the support of NASA Grant NCC2-275 has been focused in the main on the development of fuzzy logic and soft computing methodologies and their applications to systems analysis and control. with emphasis 011 problem areas which are of relevance to NASA's missions. One of the principal results of our research has been the development of a new methodology called Computing with Words (CW). Basically, in CW words drawn from a natural language are employed in place of numbers for computing and reasoning. There are two major imperatives for computing with words. First, computing with words is a necessity when the available information is too imprecise to justify the use of numbers, and second, when there is a tolerance for imprecision which can be exploited to achieve tractability, robustness, low solution cost, and better rapport with reality. Exploitation of the tolerance for imprecision is an issue of central importance in CW.
Morphology analysis of EKG R waves using wavelets with adaptive parameters derived from fuzzy logic
NASA Astrophysics Data System (ADS)
Caldwell, Max A.; Barrington, William W.; Miles, Richard R.
Understanding of the EKG components P, QRS (R wave), and T is essential in recognizing cardiac disorders and arrhythmias. An estimation method is presented that models the R wave component of the EKG by adaptively computing wavelet parameters using fuzzy logic. The parameters are adaptively adjusted to minimize the difference between the original EKG waveform and the wavelet. The R wave estimate is derived from minimizing the combination of mean squared error (MSE), amplitude difference, spread difference, and shift difference. We show that the MSE in both non-noise and additive noise environment is less using an adaptive wavelet than a static wavelet. Research to date has focused on the R wave component of the EKG signal. Extensions of this method to model P and T waves are discussed.
A Fuzzy Logic Enhanced Environmental Protection Education Model for Policies Decision Support in Green Community Development
This study proposes the promotion of environmental protection education among communities as a solution to the serious problems of high energy consumption and carbon emissions around the world. Environmental protection education has direct and lasting influences on everyone in society; therefore, it is helpful in our fight against many serious problems caused by high energy consumption. In this study, the Delphi method and the fuzzy logic theory are used to develop a quantizing assessment model based on qualitative analysis. This model can be used to assess the results and influences of community residents' participation in environmental protection education on green community development. In addition, it can be used to provide references for governing authorities in their decision making of green community development policies. PMID:24363614
Design and Construction of Intelligent Traffic Light Control System Using Fuzzy Logic
NASA Astrophysics Data System (ADS)
Lin, Htin; Aye, Khin Muyar; Tun, Hla Myo; Theingi, Naing, Zaw Min
Vehicular travel is increasing throughout the world, particularly in large urban areas. Therefore the need arises for simulation and optimizing traffic control algorithms to better accommodate this increasing demand. This paper presents a microcontroller simulation of intelligent traffic light controller using fuzzy logic that is used to change the traffic signal cycles adaptively at a two-way intersection. This paper is an attempt to design an intelligent traffic light control systems using microcontrollers such as PIC 16F84A and PIC 16F877A. And then traffic signal can be controlled depending upon the densities of cars behind green and red lights of the two-way intersection by using sensors and detectors circuits.
Energy management strategy based on fuzzy logic for a fuel cell hybrid bus
NASA Astrophysics Data System (ADS)
Gao, Dawei; Jin, Zhenhua; Lu, Qingchun
Fuel cell vehicles, as a substitute for internal-combustion-engine vehicles, have become a research hotspot for most automobile manufacturers all over the world. Fuel cell systems have disadvantages, such as high cost, slow response and no regenerative energy recovery during braking; hybridization can be a solution to these drawbacks. This paper presents a fuel cell hybrid bus which is equipped with a fuel cell system and two energy storage devices, i.e., a battery and an ultracapacitor. An energy management strategy based on fuzzy logic, which is employed to control the power flow of the vehicular power train, is described. This strategy is capable of determining the desired output power of the fuel cell system, battery and ultracapacitor according to the propulsion power and recuperated braking power. Some tests to verify the strategy were developed, and the results of the tests show the effectiveness of the proposed energy management strategy and the good performance of the fuel cell hybrid bus.
A comparison of fuzzy logic-PID control strategies for PWR pressurizer control
Kavaklioglu, K.; Ikonomopoulos, A. )
This paper describes the results obtained from a comparison performed between classical proportional-integral-derivative (PID) and fuzzy logic (FL) controlling the pressure in a pressurized water reactor (PWR). The two methodologies have been tested under various transient scenarios, and their performances are evaluated with respect to robustness and on-time response to external stimuli. One of the main concerns in the safe operation of PWR is the pressure control in the primary side of the system. In order to maintain the pressure in a PWR at the desired level, the pressurizer component equipped with sprayers, heaters, and safety relief valves is used. The control strategy in a Westinghouse PWR is implemented with a PID controller that initiates either the electric heaters or the sprayers, depending on the direction of the coolant pressure deviation from the setpoint.
PID self tuning control based on Mamdani fuzzy logic control for quadrotor stabilization
NASA Astrophysics Data System (ADS)
Priyambodo, Tri Kuntoro; Dharmawan, Andi; Putra, Agfianto Eko
Quadrotor as one type of UAV have the ability to perform Vertical Take Off and Landing (VTOL). It allows the Quadrotor to be stationary hovering in the air. PID (Proportional Integral Derivative) control system is one of the control methods that are commonly used. It is usually used to optimize the Quadrotor stabilization at least based on the three Eulerian angles (roll, pitch, and yaw) as input parameters for the control system. The three constants of PID can be obtained in various methods. The simplest method is tuning manually. This method has several weaknesses. For example if the three constants are not exact, the resulting response will deviate from the desired result. By combining the methods of PID with fuzzy logic systems where human expertise is implemented into the machine language is expected to further optimize the control system.
Posture recognition based on fuzzy logic for home monitoring of the elderly.
Brulin, Damien; Benezeth, Yannick; Courtial, Estelle
We propose in this paper a computer vision-based posture recognition method for home monitoring of the elderly. The proposed system performs human detection prior to the posture analysis; posture recognition is performed only on a human silhouette. The human detection approach has been designed to be robust to different environmental stimuli. Thus, posture is analyzed with simple and efficient features that are not designed to manage constraints related to the environment but only designed to describe human silhouettes. The posture recognition method, based on fuzzy logic, identifies four static postures and is robust to variation in the distance between the camera and the person, and to the person's morphology. With an accuracy of 74.29% of satisfactory posture recognition, this approach can detect emergency situations such as a fall within a health smart home. PMID:22997188
Words: blocks, amoebas, or patches of fog? Artificial intelligence and the conceptual foundations of fuzzy logic
NASA Astrophysics Data System (ADS)
Jaki, Stanley L.
Words are the fundamental carriers of information. Words that refer to numbers stand apart from all other words in one respect: Numbers are concepts that lend themselves to spatial representations with exact contours. Yet the verbal definition of numbers, through which their meaning is defined, shares in a property common to all words: their verbal definition cannot be given a spatial representation with exact contours. In that definitional respect, words are not even comparable to amoebas which, although they constantly change their shapes, have clear boundaries. Words are best to be likened to patches of fog that not only change but have no strict boundaries. While this does not land all discourse in the realm of half-truths, it sets basic limits to what can be achieved by fuzzy logic and programs of artificial intelligence.
Fuzzy logic control algorithms for MagneShock semiactive vehicle shock absorbers: design and experimental evaluations
NASA Astrophysics Data System (ADS)
Craft, Michael J.; Buckner, Gregory D.; Anderson, Richard D.
Automotive ride quality and handling performance remain challenging design tradeoffs for modern, passive automobile suspension systems. Despite extensive published research outlining the benefits of active vehicle suspensions in addressing this tradeoff, the cost and complexity of these systems frequently prohibit commercial adoption. Semi-active suspensions can provide performance benefits over passive suspensions without the cost and complexity associated with fully active systems. This paper outlines the development and experimental evaluation of a fuzzy logic control algorithm for a commercial semi-active suspension component, Carrera's MagneShockTM shock absorber. The MagneShockTM utilizes an electromagnet to change the viscosity of magnetorheological (MR) fluid, which changes the damping characteristics of the shock. Damping for each shock is controlled by manipulating the coil current using real-time algorithms. The performance capabilities of fuzzy logic control (FLC) algorithms are demonstrated through experimental evaluations on a passenger vehicle. Results show reductions of 25% or more in sprung mass absorbed power (U.S. Army 6 Watt Absorbed Power Criterion) as compared to typical passive shock absorbers over urban terrains in both simulation and experimentation. Average sprung-mass RMS accelerations were also reduced by as much as 9%, but usually with an increase in total suspension travel over the passive systems. Additionally, a negligible decrease in RMS tire normal force was documented through computer simulations. And although the FLC absorbed power was comparable to that of the fixed-current MagneShockTM the FLC revealed reduced average RMS sprung-mass accelerations over the fixed-current MagneShocks by 2-9%. Possible means for improvement of this system include reducing the suspension spring stiffness and increasing the dynamic damping range of the MagneShockTM.
Search and selection hotel system in Surabaya based on geographic information system (GIS) with fuzzy logic
NASA Astrophysics Data System (ADS)
Purbandini, Taufik
Surabaya is a metropolitan city in Indonesia. When the visitor has an interest in Surabaya for several days, then the visitor was looking for lodging that is closest to the interests of making it more efficient and practical. It was not a waste of time for the businessman because of congestion and so we need full information about the hotel as an inn during a stay in Surabaya began name, address of the hotel, the hotel's website, the distance from the hotel to the destination until the display of the map along the route with the help of Google Maps. This system was designed using fuzzy logic which aims to assist the user in making decisions. Design of hotel search and selection system was done through four stages. The first phase was the collection of data and as the factors that influence the decision-making along with the limit values of these factors. Factors that influence covers a distance of the hotel, the price of hotel rooms, and hotel reviews. The second stage was the processing of data and information by creating membership functions. The third stage was the analysis of systems with fuzzy logic. The steps were performed in systems analysis, namely fuzzification, inference using Mamdani, and defuzzification. The last stage was the design and construction of the system. Designing the system using use case diagrams and activity diagram to describe any process that occurs. Development system includes system implementation and evaluation systems. Implementation of mobile with Android-based system so that these applications were user friendly.
Validation of fuzzy logic method for automated mass spectral classification for mineral imaging
NASA Astrophysics Data System (ADS)
Yan, B.; McJunkin, T. R.; Stoner, D. L.; Scott, J. R.
Imaging mass spectrometry requires the acquisition and interpretation of hundreds to thousands of individual spectra in order to map the mineral phases within heterogeneous geomatrices. A fuzzy logic inference engine (FLIE) was developed to automate data interpretation. To evaluate the strengths and limitations of FLIE, the chemical images obtained using FLIE were compared with those developed using two chemometric methods: principle component analysis (PCA) and cluster analysis (K-Means). Two heterogeneous geomatrices, a low-grade chalcopyrite ore and basalt, were imaged using a laser-desorption Fourier transform mass spectrometer. Similar mineral distribution patterns in the chalcopyrite ore sample were obtained by the three data analysis methods with most of the differences occurring at the interfaces between mineral phases. PCA missed one minor mineral phase in the chalcopyrite ore sample and did not clearly differentiate among the mineral classes of the basalt. K-Means cluster analysis differentiated among the various mineral phases in both samples, but improperly grouped some spectra in the chalcopyrite sample that only contained unanticipated high mass peaks. Unlike the chemometric methods, FLIE was able to classify spectra as unknowns for those spectra that fell below the confidence level threshold. A nearest neighbor approach, included in FLIE, was used to classify the unknowns to form a visually complete image; however, the unknowns identified by FLIE can be informative because they highlight potential problems or overlooked results. In conclusion, this study validated the fuzzy logic-based approach used in our laboratory and reveald some limitations in the three techniques that were evaluated.
Validation of Fuzzy Logic Method for Automated Mass Spectral Classification for Mineral Imaging
B. Yan; B. Yan; T. R. McJunkiin; D. L. Stoner
Imaging mass spectrometry requires the acquisition and interpretation of hundreds to thousands of individual spectra in order to map the mineral phases within heterogeneous geomatrices. A fuzzy logic inference engine (FLIE) was developed to automate data interpretation. To evaluate the strengths and limitations of FLIE, the chemical images obtained using FLIE were compared with those developed using two chemometric methods: principle component analysis (PCA) and cluster analysis (K-Means). Two heterogeneous geomatrices, a low-grade chalcopyrite ore and basalt, were imaged using a laser-desorption Fourier transform mass spectrometer. Similar mineral distribution patterns in the chalcopyrite ore sample were obtained by the three data analysis methods with most of the differences occurring at the interfaces between mineral phases. PCA missed one minor mineral phase in the chalcopyrite ore sample and did not clearly differentiate among the mineral classes of the basalt. K-Means cluster analysis differentiated among the various mineral phases in both samples, but improperly grouped some spectra in the chalcopyrite sample that only contained unanticipated high mass peaks. Unlike the chemometric methods, FLIE was able to classify spectra as unknowns for those spectra that fell below the confidence level threshold. A nearest neighbor approach, included in FLIE, was used to classify the unknowns to form a visually complete image; however, the unknowns identified by FLIE can be informative because they highlight potential problems or overlooked results. In conclusion, this study validated the fuzzy logic-based approach used in our laboratory and reveald some limitations in the three techniques that were evaluated.
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
Tahmasebi, Pejman; Hezarkhani, Ardeshir
The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) – as a well-known technique to solve the complex optimization problems – is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS–GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS–GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems. PMID:25540468
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
NASA Astrophysics Data System (ADS)
Tahmasebi, Pejman; Hezarkhani, Ardeshir
The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.
Fuzzy logic system able to detect interesting areas of a video sequence
NASA Astrophysics Data System (ADS)
De Vleeschouwer, Christophe; Marichal, Xavier; Delmot, Thierry; Macq, Benoit M. M.
This paper introduces an automatic tool able to analyze the picture according to the semantic interest an observer attributes to its content. Its aim is to give a 'level of interest' to the distinct areas of the picture extracted by any segmentation tool. For the purpose of dealing with semantic interpretation of images, a single criterion is clearly insufficient because the human brain, due to its a priori knowledge and its huge memory of real-world concrete scenes, combines different subjective criteria in order to assess its final decision. The developed method permits such combination through a model using assumptions to express some general subjective criteria. Fuzzy logic enables the user to encode knowledge in a form that is very close the way experts think about the decision process. This fuzzy modeling is also well suited to represent multiple collaborating or even conflicting experts opinions. Actually, the assumptions are verified through a non-hierarchical strategy that considers them in a random order, each partial result contributing to the final one. Presented results prove that the tool is effective for a wide range of natural pictures. It is versatile and flexible in that it can be used stand-alone or can take into account any a priori knowledge about the scene.
Optimization of fertirrigation efficiency in strawberry crops by application of fuzzy logic techniques.
de la Torre, M L; Grande, J A; Aroba, J; Andujar, J M
A high level of price support has favoured intensive agriculture and an increasing use of fertilisers and pesticides. This has resulted in the pollution of water and soils and damage to certain eco-systems. The target relationship that must be established between agriculture and environment can be called "sustainable agriculture". In this work we aim at relating strawberry total yield with nitrate concentration in water at different soil depths. To achieve this objective, we have used the Predictive Fuzzy Rules Generator (PreFuRGe) tool, based on fuzzy logic and data mining, by means of which the dose that allows a balance between yield and environmental damage minimization can be determined. This determination is quite simple and is done directly from the obtained charts. This technique can be used in other types of crops permitting one to determine in a precise way at which depth the appropriate dose of nitrate fertilizer must be correctly applied, on the one hand providing the maximum yield but, on the other hand, with the minimum loss of nitrates that leachate through the saturated zone polluting aquifers. PMID:16252058
Medical diagnosis imaging systems: image and signal processing applications aided by fuzzy logic
NASA Astrophysics Data System (ADS)
Hata, Yutaka
First, we describe an automated procedure for segmenting an MR image of a human brain based on fuzzy logic for diagnosing Alzheimer's disease. The intensity thresholds for segmenting the whole brain of a subject are automatically determined by finding the peaks of the intensity histogram. After these thresholds are evaluated in a region growing, the whole brain can be identified. Next, we describe a procedure for decomposing the obtained whole brain into the left and right cerebral hemispheres, the cerebellum and the brain stem. Our method then identified the whole brain, the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem. Secondly, we describe a transskull sonography system that can visualize the shape of the skull and brain surface from any point to examine skull fracture and some brain diseases. We employ fuzzy signal processing to determine the skull and brain surface. The phantom model, the animal model with soft tissue, the animal model with brain tissue, and a human subjects' forehead is applied in our system. The all shapes of the skin surface, skull surface, skull bottom, and brain tissue surface are successfully determined.
Fuzzy logic path planning system for collision avoidance by an autonomous rover vehicle
NASA Technical Reports Server (NTRS)
Murphy, Michael G.
The Space Exploration Initiative of the United States will make great demands upon NASA and its limited resources. One aspect of great importance will be providing for autonomous (unmanned) operation of vehicles and/or subsystems in space flight and surface exploration. An additional, complicating factor is that much of the need for autonomy of operation will take place under conditions of great uncertainty or ambiguity. Issues in developing an autonomous collision avoidance subsystem within a path planning system for application in a remote, hostile environment that does not lend itself well to remote manipulation by Earth-based telecommunications is addressed. A good focus is unmanned surface exploration of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. Four major issues addressed are (1) avoidance of a fuzzy moving obstacle; (2) backoff from a deadend in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system. Examples of the need for collision avoidance by an autonomous rover vehicle on the surface of Mars with a moving obstacle would be wind-blown debris, surface flow or anomalies due to subsurface disturbances, another vehicle, etc. The other issues of backoff, sensor fusion, and adaptive learning are important in the overall path planning system.
A fuzzy-logic-based tracker for a homing guided missile
NASA Astrophysics Data System (ADS)
Gad, Ahmed S.; Farooq, Mohammad
The research conducted in the last decade in missile design has mainly focused in the area of guidance and control. Many researchers have designed interceptors with high performance; namely: ranging from the classical control to the knowledge based techniques. The homing guided missile flight simulation testbed has been developed and tested against different control systems. The missile aerodynamic model has been simulated based on NASA reports and the output aerodynamic coefficients have been compared and justified by the wind tunnel tests as well. The other missile modules have been simulated and compared to the real missile modules in terms of input/output experimental results. The guidance and control system has yield excellent performance against incoming and outgoing maneuvering targets falling within the missile's destruction zone. However, all the test scenarios assumed that the target information from the missile seeker (tracker) is exact and obtained from the observations without any major difficulty. In the case of high density clutter and false alarms as well as the low signal-to-noise-ratio (SNR) which may be due to the existence of flair, decoy or any other counter measure, the tracker accuracy plays an important role in the over all engagement scenario. In this paper, a fuzzy logic-based technique has been employed to improve the performance of the missile seeker at high density clutter and low SNR. The Interacting Multiple Model Fuzzy Data Association (IMM-FDA) has been employed to improve the missile-target intercept accuracy.
Fuzzy Logic Based Edge Detection in Smooth and Noisy Clinical Images
Haq, Izhar
Edge detection has beneficial applications in the fields such as machine vision, pattern recognition and biomedical imaging etc. Edge detection highlights high frequency components in the image. Edge detection is a challenging task. It becomes more arduous when it comes to noisy images. This study focuses on fuzzy logic based edge detection in smooth and noisy clinical images. The proposed method (in noisy images) employs a 3×3 mask guided by fuzzy rule set. Moreover, in case of smooth clinical images, an extra mask of contrast adjustment is integrated with edge detection mask to intensify the smooth images. The developed method was tested on noise-free, smooth and noisy images. The results were compared with other established edge detection techniques like Sobel, Prewitt, Laplacian of Gaussian (LOG), Roberts and Canny. When the developed edge detection technique was applied to a smooth clinical image of size 270×290 pixels having 24 dB 'salt and pepper' noise, it detected very few (22) false edge pixels, compared to Sobel (1931), Prewitt (2741), LOG (3102), Roberts (1451) and Canny (1045) false edge pixels. Therefore it is evident that the developed method offers improved solution to the edge detection problem in smooth and noisy clinical images. PMID:26407133
A fuzzy logic based-method for prognostic decision making in breast and prostate cancers.
Seker, Huseyin; Odetayo, Michael O; Petrovic, Dobrila; Naguib, Raouf N G
Accurate and reliable decision making in oncological prognosis can help in the planning of suitable surgery and therapy, and generally, improve patient management through the different stages of the disease. In recent years, several prognostic markers have been used as indicators of disease progression in oncology. However, the rapid increase in the discovery of novel prognostic markers resulting from the development in medical technology, has dictated the need for developing reliable methods for extracting clinically significant markers where complex and nonlinear interactions between these markers naturally exist. The aim of this paper is to investigate the fuzzy k-nearest neighbor (FK-NN) classifier as a fuzzy logic method that provides a certainty degree for prognostic decision and assessment of the markers, and to compare it with: 1) logistic regression as a statistical method and 2) multilayer feedforward backpropagation neural networks an artificial neural-network tool, the latter two techniques having been widely used for oncological prognosis. In order to achieve this aim, breast and prostate cancer data sets are considered as benchmarks for this analysis. The overall results obtained indicate that the FK-NN-based method yields the highest predictive accuracy, and that it has produced a more reliable prognostic marker model than the statistical and artificial neural-network-based methods. PMID:12834167
Combining geographic information system, multicriteria evaluation techniques and fuzzy logic in siting MSW landfills
NASA Astrophysics Data System (ADS)
Gemitzi, Alexandra; Tsihrintzis, Vassilios A.; Voudrias, Evangelos; Petalas, Christos; Stravodimos, George
This study presents a methodology for siting municipal solid waste landfills, coupling geographic information systems (GIS), fuzzy logic, and multicriteria evaluation techniques. Both exclusionary and non-exclusionary criteria are used. Factors, i.e., non-exclusionary criteria, are divided in two distinct groups which do not have the same level of trade off. The first group comprises factors related to the physical environment, which cannot be expressed in terms of monetary cost and, therefore, they do not easily trade off. The second group includes those factors related to human activities, i.e., socioeconomic factors, which can be expressed as financial cost, thus showing a high level of trade off. GIS are used for geographic data acquisition and processing. The analytical hierarchy process (AHP) is the multicriteria evaluation technique used, enhanced with fuzzy factor standardization. Besides assigning weights to factors through the AHP, control over the level of risk and trade off in the siting process is achieved through a second set of weights, i.e., order weights, applied to factors in each factor group, on a pixel-by-pixel basis, thus taking into account the local site characteristics. The method has been applied to Evros prefecture (NE Greece), an area of approximately 4,000 km2. The siting methodology results in two intermediate suitability maps, one related to environmental and the other to socioeconomic criteria. Combination of the two intermediate maps results in the final composite suitability map for landfill siting.
Towards the Application of Fuzzy Logic for Developing a Novel Indoor Air Quality Index (FIAQI)
JAVID, Allahbakhsh; HAMEDIAN, Amir Abbas; GHARIBI, Hamed; SOWLAT, Mohammad Hossein
Background: In the past few decades, Indoor Air Pollution (IAP) has become a primary concern to the point. It is increasingly believed to be of equal or greater importance to human health compared to ambient air. However, due to the lack of comprehensive indices for the integrated assessment of indoor air quality (IAQ), we aimed to develop a novel, Fuzzy-Based Indoor Air Quality Index (FIAQI) to bridge the existing gap in this area. Methods: We based our index on fuzzy logic, which enables us to overcome the limitations of traditional methods applied to develop environmental quality indices. Fifteen parameters, including the criteria air pollutants, volatile organic compounds, and bioaerosols were included in the FIAQI due mainly to their significant health effects. Weighting factors were assigned to the parameters based on the medical evidence available in the literature on their health effects. The final FIAQI consisted of 108 rules. In order to demonstrate the performance of the index, data were intentionally generated to cover a variety of quality levels. In addition, a sensitivity analysis was conducted to assess the validity of the index. Results: The FIAQI tends to be a comprehensive tool to classify IAQ and produce accurate results. Conclusion: It seems useful and reliable to be considered by authorities to assess IAQ environments. PMID:27114985
Fuzzy logic based melting layer recognition from 3 GHz dual polarization radar: appraisal with NWP model and radio sounding observations
NASA Astrophysics Data System (ADS)
Islam, Tanvir; Rico-Ramirez, Miguel A.; Han, Dawei; Bray, Michaela; Srivastava, Prashant K.
The advent of polarimetry makes it possible to categorize hydrometeor inferences more accurately by providing detailed information of the scattering properties. In light of this, the authors have developed a fuzzy logic based system for the recognition of melting layer in the atmosphere. The fuzzy system is based on characterizing melting layer scatterers from non-melting scatterers using five crisp inputs, namely, horizontal reflectivity ( Z H), differential reflectivity ( Z DR), co-polar correlation coefficient ( ρ HV), linear depolarization ratio (LDR) and height of radar measurements ( H). For the implementation of melting layer recognition, the study employs the dual polarized signatures from the 3 GHz Chilbolton Advanced Meteorological Radar (CAMRA). Furthermore, a simple but effective averaging procedure for melting level estimation from a volume RHI scan is proposed. The proposed scheme has been evaluated with Weather Research and Forecasting (WRF) model simulated and radio soundings retrieved melting level height over a total of 84 RHI scan-based bright band cases. The results confirm that the estimated melting level heights from the proposed method are in good agreement with the WRF model and radio sounding observations. The 3 GHz radar melting level height estimates correspond with the R 2 and RMSE values of 0.92 and 0.24 km, respectively, when compared to the radio soundings, and 0.93 and 0.21 km, respectively, when compared to the WRF model results. Moreover, the related R 2 and RMSE values are reported as 0.93 and 0.22 km respectively between the WRF and radio soundings retrievals. This implies that the downscaled WRF modelled melting level height may also be used for operational or research needs.
Fuzzy Logic Controller Architecture for Water Level Control in Nuclear Power Plant Steam Generator (SG) Using ANFIS Training Method
Vosoughi, Naser; Naseri, Zahra
Since suitable control of water level can greatly enhance the operation of a power station, a Fuzzy logic controller architecture is applied to show desired control of the water level in a Nuclear steam generator. with regard to the physics of the system, it is shown that two inputs, a single output and the least number of rules (9 rules) are considered for a controller, and the ANFIS training method is employed to model functions in a controlled system. By using ANFIS training method, initial member functions will be trained and appropriate functions are generated to control water level inside the steam generators while using the stated rules. The proposed architecture can construct an input output mapping based on both human knowledge (in from of Fuzzy if then rules) and stipulated input output data. In this paper with a simple test it has been shown that the architecture fuzzy logic controller has a reasonable response to one step input at a constant power. Through computer simulation, it is found that Fuzzy logic controller is suitable, especially for the water level deviation and abrupt steam flow disturbances that are typical in the existing power plant. (authors)
Pore Pressure prediction in shale gas reservoirs using neural network and fuzzy logic with an application to Barnett Shale.
NASA Astrophysics Data System (ADS)
Aliouane, Leila; Ouadfeul, Sid-Ali; Boudella, Amar
The main goal of the proposed idea is to use the artificial intelligence such as the neural network and fuzzy logic to predict the pore pressure in shale gas reservoirs. Pore pressure is a very important parameter that will be used or estimation of effective stress. This last is used to resolve well-bore stability problems, failure plan identification from Mohr-Coulomb circle and sweet spots identification. Many models have been proposed to estimate the pore pressure from well-logs data; we can cite for example the equivalent depth model, the horizontal model for undercompaction called the Eaton's model…etc. All these models require a continuous measurement of the slowness of the primary wave, some thing that is not easy during well-logs data acquisition in shale gas formtions. Here, we suggest the use the fuzzy logic and the multilayer perceptron neural network to predict the pore pressure in two horizontal wells drilled in the lower Barnett shale formation. The first horizontal well is used for the training of the fuzzy set and the multilayer perecptron, the input is the natural gamma ray, the neutron porosity, the slowness of the compression and shear wave, however the desired output is the estimated pore pressure using Eaton's model. Data of another horizontal well are used for generalization. Obtained results clearly show the power of the fuzzy logic system than the multilayer perceptron neural network machine to predict the pore pressure in shale gas reservoirs. Keywords: artificial intelligence, fuzzy logic, pore pressure, multilayer perecptron, Barnett shale.
Landslide susceptibility modelling using Fuzzy Logic in the oued Larbaa basin (Oriental Rif, Morocco)
NASA Astrophysics Data System (ADS)
Zezere, J. L.; Sadiki, A.; Faleh, A.; Elkoulali, E.; Garcia, R. A. C.; Oliveira, S. C.
Together with flash floods and soil erosion, landslides are relevant natural hazards that affect marly slopes in the oued Larbaa basin, located in the Oriental Rif, Morocco. Landslides have been generated important economic, social and ecological effects, by the destruction of farming lands, and by the collapse and interruption of roads and other human infrastructures (e.g., houses). The reduction of socio-economic losses due to landslide activity needs to be accomplished through the implementation of a comprehensive mitigation landslide risk program. The first task of this program is the definition of landslide susceptible areas based on the study of relationships between spatial distribution of past landslides and the cartographic set of landslide predisposing factors. Therefore, the major aim of this work is to create a landslide susceptibility map for the study area. The oued Larbaa basin, located northwards the Taza city, has an area of 245 km2 and the elevation ranges between 450 m and 1300 m. Morphology is characterized by rounded hills cutting marly formations essentially of Cretaceous age. Land use is dominated by cereal cultures and a few sparse tree plantations. Natural vegetation shows a very high level of degradation and usually appears as shrub tufts. The inventory of instability events has been made for the study area and it includes both rainfall-triggered rotational and shallow translational slides. These landslides were included into a GIS database that comprises also the following landslide predisposing factors: slope angle, aspect and curvature, inverse wetness index, lithology and land use. The susceptibility assessment was carried out for each type of landslide (rotational slides and shallow translational slides) under the assumption that future landslides will occur under the same environmental patterns that generated landslides in the past. The modelling of landslide susceptibility was made using the Fuzzy Logic method (Fuzzy Algebraic
Adaptive control design for a class of nonlinear systems based on fuzzy logic systems with scalers and saturators
NASA Astrophysics Data System (ADS)
Wang, Yin-He; Luo, Liang; Fan, Yong-Qing; Zhang, Yun; Liu, Xiao-Ping; Zhang, Si-Ying
Many practical engineering applications require various types of fuzzy logic systems (FLSs) to design adaptive controllers for nonlinear systems with uncertainties. In this article, we will consider a fundamental theoretical question: is it possible to find a unified adaptive control design method suited to various types of FLSs? In order to solve this problem, we will introduce scalers and saturators at the input and output terminals of FLSs to form the extended FLSs (EFLS). The scalers and saturators have adjustable parameters. By designing the updated laws of these parameters and the estimate values of the fuzzy approximate accuracies, stable adaptive fuzzy controllers can be realised for a class of nonlinear systems with unknown homogeneous drift functions and gains. The proposed design method is only dependent on the outputs of EFLS and the above updated laws, thus increasing its adaptability. The fuzzy control scheme introduced in this article is suitable for all fuzzy systems with or without fuzzy rules. Simulations will also be used to show the validity of the method proposed in this article.
a Fuzzy Logic-Based Approach for the Detection of Flooded Vegetation by Means of Synthetic Aperture Radar Data
NASA Astrophysics Data System (ADS)
Tsyganskaya, V.; Martinis, S.; Twele, A.; Cao, W.; Schmitt, A.; Marzahn, P.; Ludwig, R.
In this paper an algorithm designed to map flooded vegetation from synthetic aperture radar (SAR) imagery is introduced. The approach is based on fuzzy logic which enables to deal with the ambiguity of SAR data and to integrate multiple ancillary data containing topographical information, simple hydraulic considerations and land cover information. This allows the exclusion of image elements with a backscatter value similar to flooded vegetation, to significantly reduce misclassification errors. The flooded vegetation mapping procedure is tested on a flood event that occurred in Germany over parts of the Saale catchment on January 2011 using a time series of high resolution TerraSAR-X data covering the time interval from 2009 to 2015. The results show that the analysis of multi-temporal X-band data combined with ancillary data using a fuzzy logic-based approach permits the detection of flooded vegetation areas.
FLAMEnGO: a fuzzy logic approach for methyl group assignment using NOESY and paramagnetic relaxation enhancement data.
Chao, Fa-An; Shi, Lei; Masterson, Larry R; Veglia, Gianluigi
Building on a recent method by Matthews and co-workers [1], we developed a new and efficient algorithm to assign methyl resonances from sparse and ambiguous NMR data. The new algorithm (FLAMEnGO: Fuzzy Logic Assignment of MEthyl GrOups) uses Monte Carlo sampling in conjunction with fuzzy logic to obtain the assignment of methyl resonances at high fidelity. Furthermore, we demonstrate that the inclusion of paramagnetic relaxation enhancement (PRE) data in the assignment strategy increases the percentage of correct assignments with sparse NOE data. Using synthetic tests and experimental data we show that this new approach provides up to ∼80% correct assignments with only 30% of methyl-methyl NOE data. In the experimental case of ubiquitin, PRE data from two spin labeled sites improve the percentage of assigned methyl groups up to ∼91%. This new strategy promises to further expand methyl group NMR spectroscopy to very large macromolecular systems. PMID:22134225
A controller based on Optimal Type-2 Fuzzy Logic: systematic design, optimization and real-time implementation.
Fayek, H M; Elamvazuthi, I; Perumal, N; Venkatesh, B
A computationally-efficient systematic procedure to design an Optimal Type-2 Fuzzy Logic Controller (OT2FLC) is proposed. The main scheme is to optimize the gains of the controller using Particle Swarm Optimization (PSO), then optimize only two parameters per type-2 membership function using Genetic Algorithm (GA). The proposed OT2FLC was implemented in real-time to control the position of a DC servomotor, which is part of a robotic arm. The performance judgments were carried out based on the Integral Absolute Error (IAE), as well as the computational cost. Various type-2 defuzzification methods were investigated in real-time. A comparative analysis with an Optimal Type-1 Fuzzy Logic Controller (OT1FLC) and a PI controller, demonstrated OT2FLC׳s superiority; which is evident in handling uncertainty and imprecision induced in the system by means of noise and disturbances. PMID:24962934
Fuzzy logic inference-based Pavement Friction Management and real-time slippery warning systems: A proof of concept study.
Najafi, Shahriar; Flintsch, Gerardo W; Khaleghian, Seyedmeysam
Minimizing roadway crashes and fatalities is one of the primary objectives of highway engineers, and can be achieved in part through appropriate maintenance practices. Maintaining an appropriate level of friction is a crucial maintenance practice, due to the effect it has on roadway safety. This paper presents a fuzzy logic inference system that predicts the rate of vehicle crashes based on traffic level, speed limit, and surface friction. Mamdani and Sugeno fuzzy controllers were used to develop the model. The application of the proposed fuzzy control system in a real-time slippery road warning system is demonstrated as a proof of concept. The results of this study provide a decision support model for highway agencies to monitor their network's friction and make appropriate judgments to correct deficiencies based on crash risk. Furthermore, this model can be implemented in the connected vehicle environment to warn drivers of potentially slippery locations. PMID:26914521
Temporal and Spatial prediction of groundwater levels using Artificial Neural Networks, Fuzzy logic and Kriging interpolation.
NASA Astrophysics Data System (ADS)
Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.
The purpose of this study is to examine the use of Artificial Neural Networks (ANN) combined with kriging interpolation method, in order to simulate the hydraulic head both spatially and temporally. Initially, ANNs are used for the temporal simulation of the hydraulic head change. The results of the most appropriate ANNs, determined through a fuzzy logic system, are used as an input for the kriging algorithm where the spatial simulation is conducted. The proposed algorithm is tested in an area located across Isar River in Bayern, Germany and covers an area of approximately 7800 km2. The available data extend to a time period from 1/11/2008 to 31/10/2012 (1460 days) and include the hydraulic head at 64 wells, temperature and rainfall at 7 weather stations and surface water elevation at 5 monitoring stations. One feedforward ANN was trained for each of the 64 wells, where hydraulic head data are available, using a backpropagation algorithm. The most appropriate input parameters for each wells' ANN are determined considering their proximity to the measuring station, as well as their statistical characteristics. For the rainfall, the data for two consecutive time lags for best correlated weather station, as well as a third and fourth input from the second best correlated weather station, are used as an input. The surface water monitoring stations with the three best correlations for each well are also used in every case. Finally, the temperature for the best correlated weather station is used. Two different architectures are considered and the one with the best results is used henceforward. The output of the ANNs corresponds to the hydraulic head change per time step. These predictions are used in the kriging interpolation algorithm. However, not all 64 simulated values should be used. The appropriate neighborhood for each prediction point is constructed based not only on the distance between known and prediction points, but also on the training and testing error of
Cb-LIKE - Thunderstorm forecasts up to six hours with fuzzy logic
NASA Astrophysics Data System (ADS)
Köhler, Martin; Tafferner, Arnold
Thunderstorms with their accompanying effects like heavy rain, hail, or downdrafts cause delays and flight cancellations and therefore high additional cost for airlines and airport operators. A reliable thunderstorm forecast up to several hours could provide more time for decision makers in air traffic for an appropriate reaction on possible storm cells and initiation of adequate counteractions. To provide the required forecasts Cb-LIKE (Cumulonimbus-LIKElihood) has been developed at the DLR (Deutsches Zentrum für Luft- und Raumfahrt) Institute of Atmospheric Physics. The new algorithm is an automated system which designates areas with possible thunderstorm development by using model data of the COSMO-DE weather model, which is driven by the German Meteorological Service (DWD). A newly developed "Best-Member- Selection" method allows the automatic selection of that particular model run of a time-lagged COSMO- DE model ensemble, which matches best the current thunderstorm situation. Thereby the application of the best available data basis for the calculation of the thunderstorm forecasts by Cb-LIKE is ensured. Altogether there are four different modes for the selection of the best member. Four atmospheric parameters (CAPE, vertical wind velocity, radar reflectivity and cloud top temperature) of the model output are used within the algorithm. A newly developed fuzzy logic system enables the subsequent combination of the model parameters and the calculation of a thunderstorm indicator within a value range of 12 up to 88 for each grid point of the model domain for the following six hours in one hour intervals. The higher the indicator value the more the model parameters imply the development of thunderstorms. The quality of the Cb-LIKE thunderstorm forecasts was evaluated by a substantial verification using a neighborhood verification approach and multi-event contingency tables. The verification was performed for the whole summer period of 2012. On the basis of a
A Fuzzy Logic Prompting Mechanism Based on Pattern Recognition and Accumulated Activity Effective Index Using a Smartphone Embedded Sensor.
Liu, Chung-Tse; Chan, Chia-Tai
Sufficient physical activity can reduce many adverse conditions and contribute to a healthy life. Nevertheless, inactivity is prevalent on an international scale. Improving physical activity is an essential concern for public health. Reminders that help people change their health behaviors are widely applied in health care services. However, timed-based reminders deliver periodic prompts suffer from flexibility and dependency issues which may decrease prompt effectiveness. We propose a fuzzy logic prompting mechanism, Accumulated Activity Effective Index Reminder (AAEIReminder), based on pattern recognition and activity effective analysis to manage physical activity. AAEIReminder recognizes activity levels using a smartphone-embedded sensor for pattern recognition and analyzing the amount of physical activity in activity effective analysis. AAEIReminder can infer activity situations such as the amount of physical activity and days spent exercising through fuzzy logic, and decides whether a prompt should be delivered to a user. This prompting system was implemented in smartphones and was used in a short-term real-world trial by seventeenth participants for validation. The results demonstrated that the AAEIReminder is feasible. The fuzzy logic prompting mechanism can deliver prompts automatically based on pattern recognition and activity effective analysis. AAEIReminder provides flexibility which may increase the prompts' efficiency. PMID:27548184
Fuzzy logic sensing of G-quadruplex DNA and its cleavage reagents based on reduced graphene oxide.
Huang, Wei Tao; Zhang, Jian Rong; Xie, Wan Yi; Shi, Yan; Luo, Hong Qun; Li, Nian Bing
Herein, by combining the merits of nanotechnology and fuzzy logic theory, we develop a simple, label-free, and general strategy based on an organic dye-graphene hybrid system for fluorescence intelligent sensing of G-quadruplexes (G4) formation, hydroxyl radical (HO∙), and Fe(2+) in vitro. By exploiting acridine orange (AO) dyes-graphene as a nanofilter and nanoswitch and the ability of graphene to interact with DNA with different structures, our approach can efficiently distinguish, quantitatively detect target analytes. In vitro assays with G4DNA demonstrated increases in fluorescence intensity of the AO-rGO system with a linear range of 16-338 nM and a detection limit as low as 2.0 nM. The requenched fluorescence of the G4TBA-AO-rGO system has a non-linear response to Fenton reagent. But this requenching reduces the fluorescence intensity in a manner proportional to the logarithm to the base 10 of the concentration of Fenton reagent in the range of 0.1-100 μM and 100-2000 μM, respectively. Furthermore, we develop a novel and intelligent sensing method based on fuzzy logic which mimics human reasoning, solves complex and non-linear problems, and transforms the numerical output into the language description output for potential application in biochemical systems, environmental monitoring systems, and molecular-level fuzzy logic computing system. PMID:24561526
Application of Fuzzy-Logic Controller and Neural Networks Controller in Gas Turbine Speed Control and Overheating Control and Surge Control on Transient Performance
NASA Astrophysics Data System (ADS)
Torghabeh, A. A.; Tousi, A. M.
This paper presents Fuzzy Logic and Neural Networks approach to Gas Turbine Fuel schedules. Modeling of non-linear system using feed forward artificial Neural Networks using data generated by a simulated gas turbine program is introduced. Two artificial Neural Networks are used , depicting the non-linear relationship between gas generator speed and fuel flow, and turbine inlet temperature and fuel flow respectively . Off-line fast simulations are used for engine controller design for turbojet engine based on repeated simulation. The Mamdani and Sugeno models are used to expression the Fuzzy system . The linguistic Fuzzy rules and membership functions are presents and a Fuzzy controller will be proposed to provide an Open-Loop control for the gas turbine engine during acceleration and deceleration . MATLAB Simulink was used to apply the Fuzzy Logic and Neural Networks analysis. Both systems were able to approximate functions characterizing the acceleration and deceleration schedules . Surge and Flame-out avoidance during acceleration and deceleration phases are then checked . Turbine Inlet Temperature also checked and controls by Neural Networks controller. This Fuzzy Logic and Neural Network Controllers output results are validated and evaluated by GSP software . The validation results are used to evaluate the generalization ability of these artificial Neural Networks and Fuzzy Logic controllers.
Assessment of ultrafine particles and noise measurements using fuzzy logic and data mining techniques.
Fernández-Camacho, R; Brito Cabeza, I; Aroba, J; Gómez-Bravo, F; Rodríguez, S; de la Rosa, J
This study focuses on correlations between total number concentrations, road traffic emissions and noise levels in an urban area in the southwest of Spain during the winter and summer of 2009. The high temporal correlation between sound pressure levels, traffic intensity, particle number concentrations related to traffic, black carbon and NOx concentrations suggests that noise is linked to traffic emissions as a main source of pollution in urban areas. First, the association of these different variables was studied using PreFuRGe, a computational tool based on data mining and fuzzy logic. The results showed a clear association between noise levels and road-traffic intensity for non-extremely high wind speed levels. This behaviour points, therefore, to vehicular emissions being the main source of urban noise. An analysis for estimating the total number concentration from noise levels is also proposed in the study. The high linearity observed between particle number concentrations linked to traffic and noise levels with road traffic intensity can be used to calculate traffic related particle number concentrations experimentally. At low wind speeds, there are increases in noise levels of 1 dB for every 100 vehicles in circulation. This is equivalent to 2000 cm(-3) per vehicle in winter and 500 cm(-3) in summer. At high wind speeds, wind speed could be taken into account. This methodology allows low cost sensors to be used as a proxy for total number concentration monitoring in urban air quality networks. PMID:25616226
Trabecular bone fracture healing simulation with finite element analysis and fuzzy logic.
Shefelbine, Sandra J; Augat, Peter; Claes, Lutz; Simon, Ulrich
Trabecular bone fractures heal through intramembraneous ossification. This process differs from diaphyseal fracture healing in that the trabecular marrow provides a rich vascular supply to the healing bone, there is very little callus formation, woven bone forms directly without a cartilage intermediary, and the woven bone is remodelled to form trabecular bone. Previous studies have used numerical methods to simulate diaphyseal fracture healing or bone remodelling, however not trabecular fracture healing, which involves both tissue differentiation and trabecular formation. The objective of this study was to determine if intramembraneous bone formation and remodelling during trabecular bone fracture healing could be simulated using the same mechanobiological principles as those proposed for diaphyseal fracture healing. Using finite element analysis and the fuzzy logic for diaphyseal healing, the model simulated formation of woven bone in the fracture gap and subsequent remodelling of the bone to form trabecular bone. We also demonstrated that the trabecular structure is dependent on the applied loading conditions. A single model that can simulate bone healing and remodelling may prove to be a useful tool in predicting musculoskeletal tissue differentiation in different vascular and mechanical environments. PMID:16214492
Robust Fault Detection for Aircraft Using Mixed Structured Singular Value Theory and Fuzzy Logic
NASA Technical Reports Server (NTRS)
Collins, Emmanuel G.
The purpose of fault detection is to identify when a fault or failure has occurred in a system such as an aircraft or expendable launch vehicle. The faults may occur in sensors, actuators, structural components, etc. One of the primary approaches to model-based fault detection relies on analytical redundancy. That is the output of a computer-based model (actually a state estimator) is compared with the sensor measurements of the actual system to determine when a fault has occurred. Unfortunately, the state estimator is based on an idealized mathematical description of the underlying plant that is never totally accurate. As a result of these modeling errors, false alarms can occur. This research uses mixed structured singular value theory, a relatively recent and powerful robustness analysis tool, to develop robust estimators and demonstrates the use of these estimators in fault detection. To allow qualitative human experience to be effectively incorporated into the detection process fuzzy logic is used to predict the seriousness of the fault that has occurred.
Coordinating Role of RXRα in Downregulating Hepatic Detoxification during Inflammation Revealed by Fuzzy-Logic Modeling.
Keller, Roland; Klein, Marcus; Thomas, Maria; Dräger, Andreas; Metzger, Ute; Templin, Markus F; Joos, Thomas O; Thasler, Wolfgang E; Zell, Andreas; Zanger, Ulrich M
During various inflammatory processes circulating cytokines including IL-6, IL-1β, and TNFα elicit a broad and clinically relevant impairment of hepatic detoxification that is based on the simultaneous downregulation of many drug metabolizing enzymes and transporter genes. To address the question whether a common mechanism is involved we treated human primary hepatocytes with IL-6, the major mediator of the acute phase response in liver, and characterized acute phase and detoxification responses in quantitative gene expression and (phospho-)proteomics data sets. Selective inhibitors were used to disentangle the roles of JAK/STAT, MAPK, and PI3K signaling pathways. A prior knowledge-based fuzzy logic model comprising signal transduction and gene regulation was established and trained with perturbation-derived gene expression data from five hepatocyte donors. Our model suggests a greater role of MAPK/PI3K compared to JAK/STAT with the orphan nuclear receptor RXRα playing a central role in mediating transcriptional downregulation. Validation experiments revealed a striking similarity of RXRα gene silencing versus IL-6 induced negative gene regulation (rs = 0.79; P<0.0001). These results concur with RXRα functioning as obligatory heterodimerization partner for several nuclear receptors that regulate drug and lipid metabolism. PMID:26727233
PLL Based Energy Efficient PV System with Fuzzy Logic Based Power Tracker for Smart Grid Applications.
Rohini, G; Jamuna, V
This work aims at improving the dynamic performance of the available photovoltaic (PV) system and maximizing the power obtained from it by the use of cascaded converters with intelligent control techniques. Fuzzy logic based maximum power point technique is embedded on the first conversion stage to obtain the maximum power from the available PV array. The cascading of second converter is needed to maintain the terminal voltage at grid potential. The soft-switching region of three-stage converter is increased with the proposed phase-locked loop based control strategy. The proposed strategy leads to reduction in the ripple content, rating of components, and switching losses. The PV array is mathematically modeled and the system is simulated and the results are analyzed. The performance of the system is compared with the existing maximum power point tracking algorithms. The authors have endeavored to accomplish maximum power and improved reliability for the same insolation of the PV system. Hardware results of the system are also discussed to prove the validity of the simulation results. PMID:27294189
A novel fuzzy logic-based image steganography method to ensure medical data security.
Karakış, R; Güler, I; Çapraz, I; Bilir, E
This study aims to secure medical data by combining them into one file format using steganographic methods. The electroencephalogram (EEG) is selected as hidden data, and magnetic resonance (MR) images are also used as the cover image. In addition to the EEG, the message is composed of the doctor׳s comments and patient information in the file header of images. Two new image steganography methods that are based on fuzzy-logic and similarity are proposed to select the non-sequential least significant bits (LSB) of image pixels. The similarity values of the gray levels in the pixels are used to hide the message. The message is secured to prevent attacks by using lossless compression and symmetric encryption algorithms. The performance of stego image quality is measured by mean square of error (MSE), peak signal-to-noise ratio (PSNR), structural similarity measure (SSIM), universal quality index (UQI), and correlation coefficient (R). According to the obtained result, the proposed method ensures the confidentiality of the patient information, and increases data repository and transmission capacity of both MR images and EEG signals. PMID:26555746
An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic
NASA Astrophysics Data System (ADS)
Pulvirenti, L.; Pierdicca, N.; Chini, M.; Guerriero, L.
An algorithm developed to map flooded areas from synthetic aperture radar imagery is presented in this paper. It is conceived to be inserted in the operational flood management system of the Italian Civil Protection and can be used in an almost automatic mode or in an interactive mode, depending on the user's needs. The approach is based on the fuzzy logic that is used to integrate theoretical knowledge about the radar return from inundated areas taken into account by means of three electromagnetic scattering models, with simple hydraulic considerations and contextual information. This integration aims at allowing a user to cope with situations, such as the presence of vegetation in the flooded area, in which inundation mapping from satellite radars represents a difficult task. The algorithm is designed to work with radar data at L, C, and X frequency bands and employs also ancillary data, such as a land cover map and a digital elevation model. The flood mapping procedure is tested on an inundation that occurred in Albania on January 2010 using COSMO-SkyMed very high resolution X-band SAR data.
Automated real-time pavement distress detection using fuzzy logic and neural network
NASA Astrophysics Data System (ADS)
Cheng, Heng-Da
Conventional visual and manual pavement distress analysis approaches are very costly, time-consuming, dangerous, labor-intensive, tedious, subjective, having high degree of variability, unable to provide meaningful quantitative information, and almost always leading to inconsistencies in distress detail over space and across evaluations. In this paper, a novel system for multipurpose automated real-time pavement distress analysis based on fuzzy logic and neural networks will be studied. The proposed system can: provide high data acquisition rates; effectively and accurately identify the type, severity and extent of surface distress; improve the safety and efficiency of data collection; offer an objective standard of analysis and classification of distress; help identify cost effective maintenance and repair plans; provide images and examples through information highway to other user/researchers; provide image/sample back for training or as the benchmark for testing new algorithms. The proposed system will reduce the cost for maintenance/repair greatly, and can contribute to other research in pavement maintenance, repair and rehabilitation.
Coordinating Role of RXRα in Downregulating Hepatic Detoxification during Inflammation Revealed by Fuzzy-Logic Modeling
Thomas, Maria; Dräger, Andreas; Metzger, Ute; Templin, Markus F.; Joos, Thomas O.; Thasler, Wolfgang E.; Zell, Andreas; Zanger, Ulrich M.
During various inflammatory processes circulating cytokines including IL-6, IL-1β, and TNFα elicit a broad and clinically relevant impairment of hepatic detoxification that is based on the simultaneous downregulation of many drug metabolizing enzymes and transporter genes. To address the question whether a common mechanism is involved we treated human primary hepatocytes with IL-6, the major mediator of the acute phase response in liver, and characterized acute phase and detoxification responses in quantitative gene expression and (phospho-)proteomics data sets. Selective inhibitors were used to disentangle the roles of JAK/STAT, MAPK, and PI3K signaling pathways. A prior knowledge-based fuzzy logic model comprising signal transduction and gene regulation was established and trained with perturbation-derived gene expression data from five hepatocyte donors. Our model suggests a greater role of MAPK/PI3K compared to JAK/STAT with the orphan nuclear receptor RXRα playing a central role in mediating transcriptional downregulation. Validation experiments revealed a striking similarity of RXRα gene silencing versus IL-6 induced negative gene regulation (rs = 0.79; P<0.0001). These results concur with RXRα functioning as obligatory heterodimerization partner for several nuclear receptors that regulate drug and lipid metabolism. PMID:26727233
PLL Based Energy Efficient PV System with Fuzzy Logic Based Power Tracker for Smart Grid Applications
Rohini, G.; Jamuna, V.
This work aims at improving the dynamic performance of the available photovoltaic (PV) system and maximizing the power obtained from it by the use of cascaded converters with intelligent control techniques. Fuzzy logic based maximum power point technique is embedded on the first conversion stage to obtain the maximum power from the available PV array. The cascading of second converter is needed to maintain the terminal voltage at grid potential. The soft-switching region of three-stage converter is increased with the proposed phase-locked loop based control strategy. The proposed strategy leads to reduction in the ripple content, rating of components, and switching losses. The PV array is mathematically modeled and the system is simulated and the results are analyzed. The performance of the system is compared with the existing maximum power point tracking algorithms. The authors have endeavored to accomplish maximum power and improved reliability for the same insolation of the PV system. Hardware results of the system are also discussed to prove the validity of the simulation results. PMID:27294189
Performance evaluation of cost-based vs. fuzzy-logic-based prediction approaches in PRIDE
NASA Astrophysics Data System (ADS)
Kootbally, Z.; Schlenoff, C.; Madhavan, R.; Foufou, S.
PRIDE (PRediction In Dynamic Environments) is a hierarchical multi-resolutional framework for moving object prediction. PRIDE incorporates multiple prediction algorithms into a single, unifying framework. To date, we have applied this framework to predict the future location of autonomous vehicles during on-road driving. In this paper, we describe two different approaches to compute long-term predictions (on the order of seconds into the future) within PRIDE. The first is a cost-based approach that uses a discretized set of vehicle motions and costs associated with states and actions to compute probabilities of vehicle motion. The cost-based approach is the first prediction approach we have been using within PRIDE. The second is a fuzzy-logic-based approach that deals with the pervasive presence of uncertainty in the environment to negotiate complex traffic situations. Using the high-fidelity physics-based framework for the Unified System for Automation and Robot Simulation (USARSim), we will compare the performance of the two approaches in different driving situations at traffic intersections. Consequently, we will show how the two approaches complement each other and how their combination performs better than the cost-based approach only.
Using Fuzzy Logic Techniques for Assertion-Based Software Testing Metrics
Alakeel, Ali M.
Software testing is a very labor intensive and costly task. Therefore, many software testing techniques to automate the process of software testing have been reported in the literature. Assertion-Based automated software testing has been shown to be effective in detecting program faults as compared to traditional black-box and white-box software testing methods. However, the applicability of this approach in the presence of large numbers of assertions may be very costly. Therefore, software developers need assistance while making decision to apply Assertion-Based testing in order for them to get the benefits of this approach at an acceptable level of costs. In this paper, we present an Assertion-Based testing metrics technique that is based on fuzzy logic. The main goal of the proposed technique is to enhance the performance of Assertion-Based software testing in the presence of large numbers of assertions. To evaluate the proposed technique, an experimental study was performed in which the proposed technique is applied on programs with assertions. The result of this experiment shows that the effectiveness and performance of Assertion-Based software testing have improved when applying the proposed testing metrics technique. PMID:26060839
Novel intelligent real-time position tracking system using FPGA and fuzzy logic.
Soares dos Santos, Marco P; Ferreira, J A F
The main aim of this paper is to test if FPGAs are able to achieve better position tracking performance than software-based soft real-time platforms. For comparison purposes, the same controller design was implemented in these architectures. A Multi-state Fuzzy Logic controller (FLC) was implemented both in a Xilinx(®) Virtex-II FPGA (XC2v1000) and in a soft real-time platform NI CompactRIO(®)-9002. The same sampling time was used. The comparative tests were conducted using a servo-pneumatic actuation system. Steady-state errors lower than 4 μm were reached for an arbitrary vertical positioning of a 6.2 kg mass when the controller was embedded into the FPGA platform. Performance gains up to 16 times in the steady-state error, up to 27 times in the overshoot and up to 19.5 times in the settling time were achieved by using the FPGA-based controller over the software-based FLC controller. PMID:24112645
Fuzzy logic controller approach in quality and productivity improvement program (PPKP)
NASA Astrophysics Data System (ADS)
Ruza, Nadiah; Mustafa, Zainol; Rika Fatimah, P. L.; Hussain, Saiful Izzuan
The education sector plays a major role in building the stability and strength of a country and also the main channel in shaping the quality of nation. Each generation have different educational level. Therefore, improvements should be made on an on-going basis to ensure that quality of education is at high level all the time. In general, this study aimed to determine the effectiveness of the education system for Quality and Productivity Improvement Program (PPKP), Universiti Kebangsaan Malaysia (UKM) from the perspective of alumni as well as their satisfaction and importance level on how PPKP be able to meet the needs of their students. This study discusses the application of Fuzzy Logic Control analysis, which is flexible and adjustable. This analysis also identifies the program's quality of education system through alumni point of view. Overall, it was found that 93.4 percent of respondents felt that all four dimensions of students' needs have high level of importance. The rest felt that the importance level of all four dimensions is modest. Next, in term of satisfaction level with PPKP, only one percent was very satisfied with PPKP's role in meeting the needs of students and the rest felt that their needs are met only at moderate level. Results of this study could be used to improve the quality of education system for PPKP.
Fuzzy logic filtering of radar reflectivity to remove non-meteorological echoes using dual polarization radar moments
NASA Astrophysics Data System (ADS)
Dufton, D. R. L.; Collier, C. G.
The ability of a fuzzy logic classifier to dynamically identify non-meteorological radar echoes is demonstrated using data from the National Centre for Atmospheric Science dual polarisation, Doppler, X-band mobile radar. Dynamic filtering of radar echoes is required due to the variable presence of spurious targets, which can include insects, ground clutter and background noise. The fuzzy logic classifier described here uses novel multi-vertex membership functions which allow a range of distributions to be incorporated into the final decision. These membership functions are derived using empirical observations, from a subset of the available radar data. The classifier incorporates a threshold of certainty (25 % of the total possible membership score) into the final fractional defuzzification to improve the reliability of the results. It is shown that the addition of linear texture fields, specifically the texture of the cross-correlation coefficient, differential phase shift and differential reflectivity, to the classifier along with standard dual polarisation radar moments enhances the ability of the fuzzy classifier to identify multiple features. Examples from the Convective Precipitation Experiment (COPE) show the ability of the filter to identify insects (18 August 2013) and ground clutter in the presence of precipitation (17 August 2013). Medium-duration rainfall accumulations across the whole of the COPE campaign show the benefit of applying the filter prior to making quantitative precipitation estimates. A second deployment at a second field site (Burn Airfield, 6 October 2014) shows the applicability of the method to multiple locations, with small echo features, including power lines and cooling towers, being successfully identified by the classifier without modification of the membership functions from the previous deployment. The fuzzy logic filter described can also be run in near real time, with a delay of less than 1 min, allowing its use on future
Application of fuzzy logic techniques for the qualitative interpretation of preferences in a collective questionnaire for users of wheelchairs.
Lafuente, R; Page, A; Sánchez-Lacuesta, J; Tortosa, L
Active participation of users in the evaluation of technical aids is essential, since they are part of the interface with the system and constitute a fundamental source of design criteria. In this study, 88 active users of wheelchairs were interviewed by means of a written questionnaire about their opinion concerning the adaptation of his/her wheelchair to the office workplace. A conceptual framework was introduced linking objective measurements of the user-wheelchair interface to the subjective preferences expressed by the user. Discriminant analysis was used in order to select and quantify the importance of the most significant factors influencing the user's opinions. Fuzzy logic was introduced for the qualitative interpretation of the relationship between those significant factors, based on an inductive algorithm for generating fuzzy rules. Fuzzy logic enables a person to model the uncertainty within the subjective formulation of knowledge or opinions. From the results, a mismatch between actual performance of conventional wheelchairs and requirements of office work became evident. The proposed methods make it possible to determine reliable rules explaining subjective preferences; thus, they provide a flexible means of interpreting user questionnaires and obtaining new design criteria. PMID:9505257
Identification and removal of non-meteorological echoes in dual-polarization radar data based on a fuzzy logic algorithm
NASA Astrophysics Data System (ADS)
Ye, Bo-Young; Lee, GyuWon; Park, Hong-Mok
A major issue in radar quantitative precipitation estimation is the contamination of radar echoes by non-meteorological targets such as ground clutter, chaff, clear air echoes etc. In this study, a fuzzy logic algorithm for the identification of non-meteorological echoes is developed using optimized membership functions and weights for the dual-polarization radar located at Mount Sobaek. For selected precipitation and non-meteorological events, the characteristics of the precipitation and non-meteorological echo are derived by the probability density functions of five fuzzy parameters as functions of reflectivity values. The membership functions and weights are then determined by these density functions. Finally, the nonmeteorological echoes are identified by combining the membership functions and weights. The performance is qualitatively evaluated by long-term rain accumulation. The detection accuracy of the fuzzy logic algorithm is calculated using the probability of detection (POD), false alarm rate (FAR), and clutter-signal ratio (CSR). In addition, the issues in using filtered dual-polarization data are alleviated.
Proximity Operations for Space Situational Awareness Spacecraft Rendezvous and Maneuvering using Numerical Simulations and Fuzzy Logic
NASA Astrophysics Data System (ADS)
Carrico, T.; Langster, T.; Carrico, J.; Alfano, S.; Loucks, M.; Vallado, D.
The authors present several spacecraft rendezvous and close proximity maneuvering techniques modeled with a high-precision numerical integrator using full force models and closed loop control with a Fuzzy Logic intelligent controller to command the engines. The authors document and compare the maneuvers, fuel use, and other parameters. This paper presents an innovative application of an existing capability to design, simulate and analyze proximity maneuvers; already in use for operational satellites performing other maneuvers. The system has been extended to demonstrate the capability to develop closed loop control laws to maneuver spacecraft in close proximity to another, including stand-off, docking, lunar landing and other operations applicable to space situational awareness, space based surveillance, and operational satellite modeling. The fully integrated end-to-end trajectory ephemerides are available from the authors in electronic ASCII text by request. The benefits of this system include: A realistic physics-based simulation for the development and validation of control laws A collaborative engineering environment for the design, development and tuning of spacecraft law parameters, sizing actuators (i.e., rocket engines), and sensor suite selection. An accurate simulation and visualization to communicate the complexity, criticality, and risk of spacecraft operations. A precise mathematical environment for research and development of future spacecraft maneuvering engineering tasks, operational planning and forensic analysis. A closed loop, knowledge-based control example for proximity operations. This proximity operations modeling and simulation environment will provide a valuable adjunct to programs in military space control, space situational awareness and civil space exploration engineering and decision making processes.
FLEXnav: a fuzzy logic expert dead-reckoning system for the Segway RMP
NASA Astrophysics Data System (ADS)
Ojeda, Lauro; Raju, Mukunda; Borenstein, Johann
Most mobile robots use a combination of absolute and relative sensing techniques for position estimation. Relative positioning techniques are generally known as dead-reckoning. Many systems use odometry as their only dead-reckoning means. However, in recent years fiber optic gyroscopes have become more affordable and are being used on many platforms to supplement odometry, especially in indoor applications. Still, if the terrain is not level (i.e., rugged or rolling terrain), the tilt of the vehicle introduces errors into the conversion of gyro readings to vehicle heading. In order to overcome this problem vehicle tilt must be measured and factored into the heading computation. A unique new mobile robot is the Segway Robotics Mobility Platform (RMP). This functionally close relative of the innovative Segway Human Transporter (HT) stabilizes a statically unstable single-axle robot dynamically, based on the principle of the inverted pendulum. While this approach works very well for human transportation, it introduces as unique set of challenges to navigation equipment using an onboard gyro. This is due to the fact that in operation the Segway RMP constantly changes its forward tilt, to prevent dynamically falling over. This paper introduces our new Fuzzy Logic Expert rule-based navigation (FLEXnav) method for fusing data from multiple gyroscopes and accelerometers in order to estimate accurately the attitude (i.e., heading and tilt) of a mobile robot. The attitude information is then further fused with wheel encoder data to estimate the three-dimensional position of the mobile robot. We have further extended this approach to include the special conditions of operation on the Segway RMP. The paper presents experimental results of a Segway RMP equipped with our system and running over moderately rugged terrain.
Land cover classification of Landsat 8 satellite data based on Fuzzy Logic approach
NASA Astrophysics Data System (ADS)
Taufik, Afirah; Sakinah Syed Ahmad, Sharifah
The aim of this paper is to propose a method to classify the land covers of a satellite image based on fuzzy rule-based system approach. The study uses bands in Landsat 8 and other indices, such as Normalized Difference Water Index (NDWI), Normalized difference built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) as input for the fuzzy inference system. The selected three indices represent our main three classes called water, built- up land, and vegetation. The combination of the original multispectral bands and selected indices provide more information about the image. The parameter selection of fuzzy membership is performed by using a supervised method known as ANFIS (Adaptive neuro fuzzy inference system) training. The fuzzy system is tested for the classification on the land cover image that covers Klang Valley area. The results showed that the fuzzy system approach is effective and can be explored and implemented for other areas of Landsat data.
Robust nonlinear PID-like fuzzy logic control of a planar parallel (2PRP-PPR) manipulator.
Londhe, P S; Singh, Yogesh; Santhakumar, M; Patre, B M; Waghmare, L M
In this paper, a robust nonlinear proportional-integral-derivative (PID)-like fuzzy control scheme is presented and applied to complex trajectory tracking control of a 2PRP-PPR (P-prismatic, R-revolute) planar parallel manipulator (motion platform) with three degrees-of-freedom (DOF) in the presence of parameter uncertainties and external disturbances. The proposed control law consists of mainly two parts: first part uses a feed forward term to enhance the control activity and estimated perturbed term to compensate for the unknown effects namely external disturbances and unmodeled dynamics, and the second part uses a PID-like fuzzy logic control as a feedback portion to enhance the overall closed-loop stability of the system. Experimental results are presented to show the effectiveness of the proposed control scheme. PMID:27012441
Landslide susceptibility mapping by combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process in Dozain basin
NASA Astrophysics Data System (ADS)
Tazik, E.; Jahantab, Z.; Bakhtiari, M.; Rezaei, A.; Kazem Alavipanah, S.
Landslides are among the most important natural hazards that lead to modification of the environment. Therefore, studying of this phenomenon is so important in many areas. Because of the climate conditions, geologic, and geomorphologic characteristics of the region, the purpose of this study was landslide hazard assessment using Fuzzy Logic, frequency ratio and Analytical Hierarchy Process method in Dozein basin, Iran. At first, landslides occurred in Dozein basin were identified using aerial photos and field studies. The influenced landslide parameters that were used in this study including slope, aspect, elevation, lithology, precipitation, land cover, distance from fault, distance from road and distance from river were obtained from different sources and maps. Using these factors and the identified landslide, the fuzzy membership values were calculated by frequency ratio. Then to account for the importance of each of the factors in the landslide susceptibility, weights of each factor were determined based on questionnaire and AHP method. Finally, fuzzy map of each factor was multiplied to its weight that obtained using AHP method. At the end, for computing prediction accuracy, the produced map was verified by comparing to existing landslide locations. These results indicate that the combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process method are relatively good estimators of landslide susceptibility in the study area. According to landslide susceptibility map about 51% of the occurred landslide fall into the high and very high susceptibility zones of the landslide susceptibility map, but approximately 26 % of them indeed located in the low and very low susceptibility zones.
Improved Fuzzy Logic System to Evaluate Milk Electrical Conductivity Signals from On-Line Sensors to Monitor Dairy Goat Mastitis
Zaninelli, Mauro; Tangorra, Francesco Maria; Costa, Annamaria; Rossi, Luciana; Dell'Orto, Vittorio; Savoini, Giovanni
The aim of this study was to develop and test a new fuzzy logic model for monitoring the udder health status (HS) of goats. The model evaluated, as input variables, the milk electrical conductivity (EC) signal, acquired on-line for each gland by a dedicated sensor, the bandwidth length and the frequency and amplitude of the first main peak of the Fourier frequency spectrum of the recorded milk EC signal. Two foremilk gland samples were collected from eight Saanen goats for six months at morning milking (lactation stages (LS): 0–60 Days In Milking (DIM); 61–120 DIM; 121–180 DIM), for a total of 5592 samples. Bacteriological analyses and somatic cell counts (SCC) were used to define the HS of the glands. With negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC > 1,000,000 cells/mL, glands were classified as not healthy (NH). For each EC signal, an estimated EC value was calculated and a relative deviation was obtained. Furthermore, the Fourier frequency spectrum was evaluated and bandwidth length, frequency and amplitude of the first main peak were identified. Before using these indexes as input variables of the fuzzy logic model a linear mixed-effects model was developed to evaluate the acquired data considering the HS, LS and LS × HS as explanatory variables. Results showed that performance of a fuzzy logic model, in the monitoring of mammary gland HS, could be improved by the use of EC indexes derived from the Fourier frequency spectra of gland milk EC signals recorded by on-line EC sensors. PMID:27420069
Comparison between Neural Network and Fuzzy Logic system for Soil Moisture Estimation using Microwave Remote Sensing Data
NASA Astrophysics Data System (ADS)
Lakhankar, T. Y.; Ghedira, H.; Khanbilvardi, R. M.
Artificial neural networks and Fuzzy logic have been applied to a wide range of problems in several disciplines. They have been successfully applied to image processing, and have shown a great potential in the classification of remote sensing data. However, a successful application of these methods in remote sensing data classification requires a good comprehension of the effect of their internal parameters and especially those that are related to the algorithm structure and to the training process. In this work we report the application of backpropagation neural network and fuzzy logic in estimating the soil moisture level using Synthetic Aperture Radar (SAR) data. The potential of SAR images in spatial soil moisture estimation depends on the ability of these algorithms to define the complex relationship that exists between the backscattered energy and the moisture content of the soil. A study area located in Oklahoma (97d35'W, 36d15'N) has been chosen for this project. Several textural measures derived from Radarsat-1 images acquired in Scansar Mode during the summer of 1997 were used as input for two algorithms. The soil moisture data measured by ESTAR Instrument (Electronically Scanned Thinned Array Radiometer) during the SGP97 campaign (operated by NASA) were used as truth data in the training process. The effect of some parameters related to the training process on classification performance was investigated for both methods. The preliminary results showed that for neural networks, the variations of the number of hidden layers and the number of nodes by layer have no significant effect on classification accuracy. However, the retained threshold value used in the output layer affects significantly the overall classification. Concerning, the fuzzy logic algorithm, the preliminary results showed that the cluster radius selection have a significant effect on classification accuracy.
Improved Fuzzy Logic System to Evaluate Milk Electrical Conductivity Signals from On-Line Sensors to Monitor Dairy Goat Mastitis.
Zaninelli, Mauro; Tangorra, Francesco Maria; Costa, Annamaria; Rossi, Luciana; Dell'Orto, Vittorio; Savoini, Giovanni
The aim of this study was to develop and test a new fuzzy logic model for monitoring the udder health status (HS) of goats. The model evaluated, as input variables, the milk electrical conductivity (EC) signal, acquired on-line for each gland by a dedicated sensor, the bandwidth length and the frequency and amplitude of the first main peak of the Fourier frequency spectrum of the recorded milk EC signal. Two foremilk gland samples were collected from eight Saanen goats for six months at morning milking (lactation stages (LS): 0-60 Days In Milking (DIM); 61-120 DIM; 121-180 DIM), for a total of 5592 samples. Bacteriological analyses and somatic cell counts (SCC) were used to define the HS of the glands. With negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC > 1,000,000 cells/mL, glands were classified as not healthy (NH). For each EC signal, an estimated EC value was calculated and a relative deviation was obtained. Furthermore, the Fourier frequency spectrum was evaluated and bandwidth length, frequency and amplitude of the first main peak were identified. Before using these indexes as input variables of the fuzzy logic model a linear mixed-effects model was developed to evaluate the acquired data considering the HS, LS and LS × HS as explanatory variables. Results showed that performance of a fuzzy logic model, in the monitoring of mammary gland HS, could be improved by the use of EC indexes derived from the Fourier frequency spectra of gland milk EC signals recorded by on-line EC sensors. PMID:27420069
Applying fuzzy logic to assess human perception in relation to conservation plan efficiency measures within a biosphere reserve.
Ruíz-López, Dulce M; Aragón-Noriega, Alberto E; Luna-Gonzalez, Antonio; Gonzalez-Ocampo, Hector A
The objective of this study is to present an efficiency-perception impact assessment based upon the integration of fuzzy logic (FL) of the "Productive Reconversion" conservation program (PRP) instituted by the Mexican government, in the upper Gulf of California and the Colorado Delta Biosphere Reserve. This approach enables environmental analysts to deal with the intrinsic imprecision and ambiguity associated with people's judgments and conclusions. The application of FL to the assessment of program efficiency is illustrated in this work, demonstrating how subjective perceptions can be converted into quantitative values easy to evaluate during the decision-making process. PMID:22351598
Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy Logic
NASA Technical Reports Server (NTRS)
Lu, Thomas; Pham, Timothy; Liao, Jason
This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.
An Improved Genetic Fuzzy Logic Control Method to Reduce the Enlargement of Coal Floor Deformation in Shearer Memory Cutting Process
Tan, Chao; Xu, Rongxin; Wang, Zhongbin; Si, Lei; Liu, Xinhua
In order to reduce the enlargement of coal floor deformation and the manual adjustment frequency of rocker arms, an improved approach through integration of improved genetic algorithm and fuzzy logic control (GFLC) method is proposed. The enlargement of coal floor deformation is analyzed and a model is built. Then, the framework of proposed approach is built. Moreover, the constituents of GA such as tangent function roulette wheel selection (Tan-RWS) selection, uniform crossover, and nonuniform mutation are employed to enhance the performance of GFLC. Finally, two simulation examples and an industrial application example are carried out and the results indicate that the proposed method is feasible and efficient. PMID:27217824
An Improved Genetic Fuzzy Logic Control Method to Reduce the Enlargement of Coal Floor Deformation in Shearer Memory Cutting Process.
Tan, Chao; Xu, Rongxin; Wang, Zhongbin; Si, Lei; Liu, Xinhua
In order to reduce the enlargement of coal floor deformation and the manual adjustment frequency of rocker arms, an improved approach through integration of improved genetic algorithm and fuzzy logic control (GFLC) method is proposed. The enlargement of coal floor deformation is analyzed and a model is built. Then, the framework of proposed approach is built. Moreover, the constituents of GA such as tangent function roulette wheel selection (Tan-RWS) selection, uniform crossover, and nonuniform mutation are employed to enhance the performance of GFLC. Finally, two simulation examples and an industrial application example are carried out and the results indicate that the proposed method is feasible and efficient. PMID:27217824
A Fuzzy Logic System for Seizure Onset Detection in Intracranial EEG
Rabbi, Ahmed Fazle; Fazel-Rezai, Reza
We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved. PMID:22577370
Towards manufacturing of advanced logic devices by double-patterning
NASA Astrophysics Data System (ADS)
Koay, Chiew-seng; Halle, Scott; Holmes, Steven; Petrillo, Karen; Colburn, Matthew; van Dommelen, Youri; Jiang, Aiqin; Crouse, Michael; Dunn, Shannon; Hetzer, David; Kawakami, Shinichiro; Cantone, Jason; Huli, Lior; Rodgers, Martin; Martinick, Brian
As reported previously, the IBM Alliance has established a DETO (Double-Expose-Track-Optimized) baseline, in collaboration with ASML, TEL, and CNSE, to evaluate commercially available DETO photoresist system for the manufacturing of advanced logic devices. Although EUV lithography is the baseline strategy for <2x nm logic nodes, alternative techniques are still being pursued. The DETO technique produces pitch-split patterns capable of supporting 16 nm and 11 nm node semiconductor devices. We present the long-term monitoring performances of CD uniformity (CDU), overlay, and defectivity of our DETO process. CDU and overlay performances for controlled experiments are also presented. Two alignment schemes in DETO are compared experimentally for their effects on inter-level & intralevel overlays, and space CDU. We also experimented with methods for improving CDU, in which the CD-OptimizerTMand DoseMapperTM were evaluated separately and in tandem. Overlay improvements using the Correction Per Exposure (CPE) and the intra-field High-Order Process Correction (i-HOPC) were compared against the usual linear correction method. The effects of the exposure field size are also compared between a small field and the full field. Included in all the above, we also compare the performances derived from stack-integrated wafers and bare-Si wafers.
Optimal operating rules definition in complex water resource systems combining fuzzy logic, expert criteria and stochastic programming
NASA Astrophysics Data System (ADS)
Macian-Sorribes, Hector; Pulido-Velazquez, Manuel
This contribution presents a methodology for defining optimal seasonal operating rules in multireservoir systems coupling expert criteria and stochastic optimization. Both sources of information are combined using fuzzy logic. The structure of the operating rules is defined based on expert criteria, via a joint expert-technician framework consisting in a series of meetings, workshops and surveys carried out between reservoir managers and modelers. As a result, the decision-making process used by managers can be assessed and expressed using fuzzy logic: fuzzy rule-based systems are employed to represent the operating rules and fuzzy regression procedures are used for forecasting future inflows. Once done that, a stochastic optimization algorithm can be used to define optimal decisions and transform them into fuzzy rules. Finally, the optimal fuzzy rules and the inflow prediction scheme are combined into a Decision Support System for making seasonal forecasts and simulate the effect of different alternatives in response to the initial system state and the foreseen inflows. The approach presented has been applied to the Jucar River Basin (Spain). Reservoir managers explained how the system is operated, taking into account the reservoirs' states at the beginning of the irrigation season and the inflows previewed during that season. According to the information given by them, the Jucar River Basin operating policies were expressed via two fuzzy rule-based (FRB) systems that estimate the amount of water to be allocated to the users and how the reservoir storages should be balanced to guarantee those deliveries. A stochastic optimization model using Stochastic Dual Dynamic Programming (SDDP) was developed to define optimal decisions, which are transformed into optimal operating rules embedding them into the two FRBs previously created. As a benchmark, historical records are used to develop alternative operating rules. A fuzzy linear regression procedure was employed to
Advance flood forecasting for flood stricken Bangladesh with a fuzzy reasoning method
NASA Astrophysics Data System (ADS)
Liong, Shie-Yui; Lim, Wee-Han; Kojiri, Toshiharu; Hori, Tomoharu
An artificial Neural Network (NN) was successfully applied, in an earlier study, as a prediction tool to forecast water level at Dhaka (Bangladesh), for up to seven lead days in advance, with a high accuracy level. In addition, this high accuracy degree was accompanied with a very short computational time. Both make NN a desirable advance warming forecasting tool. In a later study, a sensitivity analysis was also performed to retain only the most sensitive gauging stations for the Dhaka station. The resulting reduction of gauging stations insignificantly affects the prediction accuracy level.The work concerning the possibility of measurement failure in any of the gauging stations during the critical flow level at Dhaka requires prediction tools which can interpret linguistic assessment of flow levels. A fuzzy logic approach is introduced with two or three membership functions, depending on necessity, for the input stations with five membership functions for the output station. Membership functions for each station are derived from their respective water level frequency distributions, after the Kohonen neural network is used to group the data into clusters. The proposed approach in deriving membership function shows a number of advances over the approach commonly used. When prediction results are compared with measured data, the prediction accuracy level is comparable with that of the data driven neural network approach.
A high-speed multiplexer-based fine-grain pipelined architecture for digital fuzzy logic controllers
NASA Astrophysics Data System (ADS)
Rashidi, Bahram; Masoud Sayedi, Sayed
Design and implementation of a high-speed multiplexer-based fine-grain pipelined architecture for a general digital fuzzy logic controller has been presented. All the operators have been designed at gate level. For the multiplication, a multiplexer-based modified Wallace tree multiplier has been designed, and for the division and addition multiplexer-based non-restoring parallel divider and multiplexer-based Manchester adder have been used, respectively. To further increase the processing speed, fine-grain pipelining technique has been employed. By using this technique, the critical path of the circuit is broken into finer pieces. Based on the proposed architecture, and by using Quartus II 9.1, a sample two-input, one-output digital fuzzy logic controller with eight rules has been successfully synthesised and implemented on Stratix II field programmable gate array. Simulations were carried out using DSP Builder in the MATLAB/Simulink tool at a maximum clock rate of 301.84 MHz.
Fuzzy logic and optical correlation-based face recognition method for patient monitoring application in home video surveillance
NASA Astrophysics Data System (ADS)
Elbouz, Marwa; Alfalou, Ayman; Brosseau, Christian
Home automation is being implemented into more and more domiciles of the elderly and disabled in order to maintain their independence and safety. For that purpose, we propose and validate a surveillance video system, which detects various posture-based events. One of the novel points of this system is to use adapted Vander-Lugt correlator (VLC) and joint-transfer correlator (JTC) techniques to make decisions on the identity of a patient and his three-dimensional (3-D) positions in order to overcome the problem of crowd environment. We propose a fuzzy logic technique to get decisions on the subject's behavior. Our system is focused on the goals of accuracy, convenience, and cost, which in addition does not require any devices attached to the subject. The system permits one to study and model subject responses to behavioral change intervention because several levels of alarm can be incorporated according different situations considered. Our algorithm performs a fast 3-D recovery of the subject's head position by locating eyes within the face image and involves a model-based prediction and optical correlation techniques to guide the tracking procedure. The object detection is based on (hue, saturation, value) color space. The system also involves an adapted fuzzy logic control algorithm to make a decision based on information given to the system. Furthermore, the principles described here are applicable to a very wide range of situations and robust enough to be implementable in ongoing experiments.
Achieving high dependability and safety in sluice automation by using a fuzzy-logic-based control system
This paper describes how sluice automation was realized, achieving the customer requirements of dependability and safety. The analysis of both the managerial and technical problems has lead ENEL-PIN to develop an innovative sluice barrage control system, whose control algorithm is fuzzy-logic-based, that can be easily configured for each plant belonging to the identified sluice barrage class. APOS 1 achieves the automation requirements of dependability and safety by using a strong and continuous H/W and S/W self-testing and plant diagnosis, implementing particular techniques in order to realize a valid protection against electromagnetic interferences and climatic influences, and implementing suited command strategies to apply safety measures in case of hazardous situations. Its main tasks are to operate up to seven sluice-gates, suited for regulation, for controlling the river level near the sluices, and to perform a broadly diagnostic activity over the whole plant and on itself, to be used both on-line and for plant maintenance. APOS application software was designed in a general way for a class of plants, so that it may be configured by the plant expert personnel by means of a simple setting of some numeric parameters, during the plant installation. The core of the control system is a fuzzy-logic-based algorithm, that proved to be fast, reliable, and easily configurable to operate on different plants, because of the natural way in which the operation personnel's knowledge can be expressed. 1 APOS - Programmable Sluice Control System.
FLAMEnGO 2.0: An enhanced fuzzy logic algorithm for structure-based assignment of methyl group resonances
NASA Astrophysics Data System (ADS)
Chao, Fa-An; Kim, Jonggul; Xia, Youlin; Milligan, Michael; Rowe, Nancy; Veglia, Gianluigi
We present an enhanced version of the FLAMEnGO (Fuzzy Logic Assignment of Methyl Group) software, a structure-based method to assign methyl group resonances in large proteins. FLAMEnGO utilizes a fuzzy logic algorithm coupled with Monte Carlo sampling to obtain a probability-based assignment of the methyl group resonances. As an input, FLAMEnGO requires either the protein X-ray structure or an NMR structural ensemble including data such as methyl-methyl NOESY, paramagnetic relaxation enhancement (PRE), methine-methyl TOCSY data. Version 2.0 of this software (FLAMEnGO 2.0) has a user-friendly graphic interface and presents improved modules that enable the input of partial assignments and additional NMR restraints. We tested the performance of FLAMEnGO 2.0 on maltose binding protein (MBP) as well as the C-subunit of the cAMP-dependent protein kinase A (PKA-C). FLAMEnGO 2.0 can be used as a standalone method or to assist in the completion of partial resonance assignments and can be downloaded at www.chem.umn.edu/groups/veglia/forms/flamengo2-form.html.
Application of artificial neural network, fuzzy logic and decision tree algorithms for modelling of streamflow at Kasol in India.
Senthil Kumar, A R; Goyal, Manish Kumar; Ojha, C S P; Singh, R D; Swamee, P K
The prediction of streamflow is required in many activities associated with the planning and operation of the components of a water resources system. Soft computing techniques have proven to be an efficient alternative to traditional methods for modelling qualitative and quantitative water resource variables such as streamflow, etc. The focus of this paper is to present the development of models using multiple linear regression (MLR), artificial neural network (ANN), fuzzy logic and decision tree algorithms such as M5 and REPTree for predicting the streamflow at Kasol located at the upstream of Bhakra reservoir in Sutlej basin in northern India. The input vector to the various models using different algorithms was derived considering statistical properties such as auto-correlation function, partial auto-correlation and cross-correlation function of the time series. It was found that REPtree model performed well compared to other soft computing techniques such as MLR, ANN, fuzzy logic, and M5P investigated in this study and the results of the REPTree model indicate that the entire range of streamflow values were simulated fairly well. The performance of the naïve persistence model was compared with other models and the requirement of the development of the naïve persistence model was also analysed by persistence index. PMID:24355836
Fault detection in non-linear systems based on type-2 fuzzy logic
NASA Astrophysics Data System (ADS)
Safarinejadian, Behrooz; Ghane, Parisa; Monirvaghefi, Hossein
This paper presents a new method for fault detection (FD) based on interval type-2 fuzzy sets. The main idea is based on a confident span using interval type-2 fuzzy systems. An estimate for upper and lower bounds of output has been taken using the designing of an optimal fuzzy system through clustering. Finally the method has been tested in two non-linear systems, a two-tank with a fluid flow and pH neutralisation process, and it is compared with a well-known method named ANFIS. Furthermore, the mathematical model and the results of simulations prove the effectiveness, usefulness and applications of our new method.
Fuzzy logic and adaptive neuro-fuzzy inference system for characterization of contaminant exposure through selected biomarkers in African catfish.
Karami, Ali; Keiter, Steffen; Hollert, Henner; Courtenay, Simon C
This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A "data trimming" approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies. PMID:22752811
A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater.
Turkdogan-Aydinol, F Ilter; Yetilmezsoy, Kaan
A MIMO (multiple inputs and multiple outputs) fuzzy-logic-based model was developed to predict biogas and methane production rates in a pilot-scale 90-L mesophilic up-flow anaerobic sludge blanket (UASB) reactor treating molasses wastewater. Five input variables such as volumetric organic loading rate (OLR), volumetric total chemical oxygen demand (TCOD) removal rate (R(V)), influent alkalinity, influent pH and effluent pH were fuzzified by the use of an artificial intelligence-based approach. Trapezoidal membership functions with eight levels were conducted for the fuzzy subsets, and a Mamdani-type fuzzy inference system was used to implement a total of 134 rules in the IF-THEN format. The product (prod) and the centre of gravity (COG, centroid) methods were employed as the inference operator and defuzzification methods, respectively. Fuzzy-logic predicted results were compared with the outputs of two exponential non-linear regression models derived in this study. The UASB reactor showed a remarkable performance on the treatment of molasses wastewater, with an average TCOD removal efficiency of 93 (+/-3)% and an average volumetric TCOD removal rate of 6.87 (+/-3.93) kg TCOD(removed)/m(3)-day, respectively. Findings of this study clearly indicated that, compared to non-linear regression models, the proposed MIMO fuzzy-logic-based model produced smaller deviations and exhibited a superior predictive performance on forecasting of both biogas and methane production rates with satisfactory determination coefficients over 0.98. PMID:20609515
Multi-Objective Trajectory Optimization by a Hierarchical Gradient Algorithm with Fuzzy Decision Logic —Application to Slew Maneuver Problems of a Flexible Space Structure—
NASA Astrophysics Data System (ADS)
Kojima, Hirohisa; Nakajima, Nobuyuki
The rest-to-rest maneuver problem of a flexible space structure is a two-point boundary value problem (TPBVP) and is solved by some gradient methods. If TPBVP is strongly restricted by constraints, TBVP becomes an ill-defined problem, and the solution meeting all constraints cannot be obtained. However, reasonable suboptimal solutions are often needed since real plant systems are necessary to be controlled. In order to obtain such suboptimal solutions, we have developed a modified version of the hierarchy gradient method by installing fuzzy decision logic. Constraints are classified into non-fuzzy constraints and fuzzy constraints according to their priorities. Fuzzy constraints having a trade-off relationship with each other are compromised reasonably by fuzzy decision logic. The usefulness of the proposed method is numerically and experimentally verified by applying it to the rest-to-rest slew maneuver problem of a flexible space structure, where fuzzy constraints are final time, sensitivity of residual vibration energy with respect to the structure frequency uncertainty and maximum bending moment at the root of the flexible appendage.
A novel fuzzy logic correctional algorithm for traction control systems on uneven low-friction road conditions
NASA Astrophysics Data System (ADS)
Li, Liang; Ran, Xu; Wu, Kaihui; Song, Jian; Han, Zongqi
The traction control system (TCS) might prevent excessive skid of the driving wheels so as to enhance the driving performance and direction stability of the vehicle. But if driven on an uneven low-friction road, the vehicle body often vibrates severely due to the drastic fluctuations of driving wheels, and then the vehicle comfort might be reduced greatly. The vibrations could be hardly removed with traditional drive-slip control logic of the TCS. In this paper, a novel fuzzy logic controller has been brought forward, in which the vibration signals of the driving wheels are adopted as new controlled variables, and then the engine torque and the active brake pressure might be coordinately re-adjusted besides the basic logic of a traditional TCS. In the proposed controller, an adjustable engine torque and pressure compensation loop are adopted to constrain the drastic vehicle vibration. Thus, the wheel driving slips and the vibration degrees might be adjusted synchronously and effectively. The simulation results and the real vehicle tests validated that the proposed algorithm is effective and adaptable for a complicated uneven low-friction road.
Analysis of microseismicity using fuzzy logic and fractals for fracture network characterization
NASA Astrophysics Data System (ADS)
Aminzadeh, F.; Ayatollahy Tafti, T.; Maity, D.; Boyle, K.; Sahimi, M.; Sammis, C. G.
The area where microseismic events occur may be correlated with the fracture network at a geothermal field. For an Enhanced Geothermal System (EGS) reservoir, an extensive fracture network with a large aerial distribution is required. Pore-pressure increase, temperature changes, volume change due to fluid withdrawal/injection and chemical alteration of fracture surfaces are all mechanisms that may explain microseismic events at a geothermal field. If these mechanisms are operative, any fuzzy cluster of the microseismic events should represent a connected fracture network. Drilling new EGS wells (both injection and production wells) in these locations may facilitate the creation of an EGS reservoir. In this article we use the fuzzy clustering technique to find the location and characteristics of fracture networks in the Geysers geothermal field. We also show that the centers of these fuzzy clusters move in time, which may represent fracture propagation or fluid movement within the fracture network. Furthermore, analyzing the distribution of fuzzy hypocenters and quantifying their fractal structure helps us to develop an accurate fracture map for the reservoir. Combining the fuzzy clustering results with the fractal analysis allows us to better understand the mechanisms for fracture stimulation and better characterize the evolution of the fracture network. We also show how micro-earthquake date collected in different time periods can be correlated with drastic changes in the distribution of active fractures resulting from injection, production or other transient events.
Smart molecules at work--mimicking advanced logic operations.
Andréasson, Joakim; Pischel, Uwe
Molecular logic is an interdisciplinary research field, which has captured worldwide interest. This tutorial review gives a brief introduction into molecular logic and Boolean algebra. This serves as the basis for a discussion of the state-of-the-art and future challenges in the field. Representative examples from the most recent literature including adders/subtractors, multiplexers/demultiplexers, encoders/decoders, and sequential logic devices (keypad locks) are highlighted. Other horizons, such as the utility of molecular logic in bio-related applications, are discussed as well. PMID:20023848
Development of a Pedestrian Indoor Navigation System Based on Multi-Sensor Fusion and Fuzzy Logic Estimation Algorithms
NASA Astrophysics Data System (ADS)
Lai, Y. C.; Chang, C. C.; Tsai, C. M.; Lin, S. Y.; Huang, S. C.
This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU) has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS). There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS sensors suffer from various errors due to the results of manufacturing imperfections and other effects. Therefore, a sensor calibration procedure based on the scalar calibration and the least squares methods has been induced in this study to improve the accuracy of the inertial sensors. With the calibrated data acquired from the inertial sensors, the step length and strength of the pedestrian are estimated by multi-sensor fusion and fuzzy logic estimation algorithms. The developed multi-sensor fusion algorithm provides the amount of the walking steps and the strength of each steps in real-time. Consequently, the estimated walking amount and strength per step are taken into the proposed fuzzy logic estimation algorithm to estimates the step lengths of the user. Since the walking length and direction are both the required information of the dead reckoning navigation, the walking direction is calculated by integrating the angular rate acquired by the gyroscope of the developed IMU module. Both the walking length and direction are calculated on the IMU module and transmit to a smartphone with Bluetooth to perform the dead reckoning navigation which is run on a self-developed APP. Due to the error accumulating of dead reckoning navigation, a particle filter and a pre-loaded map of indoor environment have been applied to the APP of the proposed navigation system to extend its
Developing a new transformer fault diagnosis system through evolutionary fuzzy logic
Huang, Y.C.; Huang, C.L.; Yang, H.T.
To improve the diagnosis accuracy of the conventional dissolved gas analysis (DGA) approaches, this paper proposes an evolutionary programming (EP) based fuzzy system development technique to identify the incipient faults of the power transformers. Using the IEC/IEEE DGA criteria as references, a preliminary framework of the fuzzy diagnosis system is first built. Based on previous dissolved gas test records and their actual fault types, the proposed EP-based development technique is then employed to automatically modify the fuzzy if-then rules and simultaneously adjust the corresponding membership functions. In comparison to results of the conventional DGA and the artificial neural networks (ANN) classification methods, the proposed method has been verified to possess superior performance both in developing the diagnosis system and in identifying the practical transformer fault cases.
An Intuitionistic Fuzzy Logic Models for Multicriteria Decision Making Under Uncertainty
NASA Astrophysics Data System (ADS)
Jana, Biswajit; Mohanty, Sachi Nandan
The purpose of this paper is to enhance the applicability of the fuzzy sets for developing mathematical models for decision making under uncertainty, In general a decision making process consist of four stages, namely collection of information from various sources, compile the information, execute the information and finally take the decision/action. Only fuzzy sets theory is capable to quantifying the linguistic expression to mathematical form in complex situation. Intuitionistic fuzzy set (IFSs) which reflects the fact that the degree of non membership is not always equal to one minus degree of membership. There may be some degree of hesitation. Thus, there are some situations where IFS theory provides a more meaningful and applicable to cope with imprecise information present for solving multiple criteria decision making problem. This paper emphasis on IFSs, which is help for solving real world problem in uncertainty situation.
Establishing the overall service quality of engineering education: fuzzy logic approach
NASA Astrophysics Data System (ADS)
Shekhar, N. Chandra; Venkatasubbaiah, K.; Kandukuria, N. R.
Measuring overall service quality (OSQ) is gaining prominence in higher education due to the increased competition among engineering education institutions (EEIs) and growing awareness about value for money among the public. Determination of OSQ on certain institutional aspects is done by various agencies throughout the world. Each system uses a different set of weighted indicators to measure the overall service quality of institutions. Five service quality factors, namely professionalism, integrated education, facilities, responsiveness and empathy are considered in the study. Trapezoidal fuzzy numbers are used to determine the aggregate weights of the factors to handle the vagueness present in the linguistic values of the stakeholders' subjective opinions. Final weights of the factors are assessed by taking the distances of each factor between Fuzzy Positive Ideal Rating and Fuzzy Negative Ideal Rating. An illustrative study is presented to determine the OSQ of EEIs. The results help to focus on the factors which need immediate attention to enhance the quality of EEIs.
Closing the loop from continuous M-health monitoring to fuzzy logic-based optimized recommendations.
Benharref, Abdelghani; Serhani, Mohamed Adel; Nujum, Al Ramzana
Continuous sensing of health metrics might generate a massive amount of data. Generating clinically validated recommendations, out of these data, to patients under monitoring is of prime importance to protect them from risk of falling into severe health degradation. Physicians also can be supported with automated recommendations that gain from historical data and increasing learning cycles. In this paper, we propose a Fuzzy Expert System that relies on data collected from continuous monitoring. The monitoring scheme implements preprocessing of data for better data analytics. However, data analytics implements the loopback feature in order to constantly improve fuzzy rules, knowledge base, and generated recommendations. Both techniques reduced data quantity, improved data quality and proposed recommendations. We evaluate our solution through a series of experiments and the results we have obtained proved that our fuzzy expert system combined with the intelligent monitoring and analytic techniques provide a high accuracy of collected data and valid advices. PMID:25570547
Knowledge-based systems as decision support tools in an ecosystem approach to fisheries: Comparing a fuzzy-logic and a rule-based approach
NASA Astrophysics Data System (ADS)
Jarre, Astrid; Paterson, Barbara; Moloney, Coleen L.; Miller, David C. M.; Field, John G.; Starfield, Anthony M.
In an ecosystem approach to fisheries (EAF), management must draw on information of widely different types, and information addressing various scales. Knowledge-based systems assist in the decision-making process by summarising this information in a logical, transparent and reproducible way. Both rule-based Boolean and fuzzy-logic models have been used successfully as knowledge-based decision support tools. This study compares two such systems relevant to fisheries management in an EAF developed for the southern Benguela. The first is a rule-based system for the prediction of anchovy recruitment and the second is a fuzzy-logic tool to monitor implementation of an EAF in the sardine fishery. We construct a fuzzy-logic counterpart to the rule-based model, and a rule-based counterpart to the fuzzy-logic model, compare their results, and include feedback from potential users of these two decision support tools in our evaluation of the two approaches. With respect to the model objectives, no method clearly outperformed the other. The advantages of numerically processing continuous variables, and interpreting the final output, as in fuzzy-logic models, can be weighed up against the advantages of using a few, qualitative, easy-to-understand categories as in rule-based models. The natural language used in rule-based implementations is easily understood by, and communicated among, users of these systems. Users unfamiliar with fuzzy-set theory must "trust" the logic of the model. Graphical visualization of intermediate and end results is an important advantage of any system. Applying the two approaches in parallel improved our understanding of the model as well as of the underlying problems. Even for complex problems, small knowledge-based systems such as the ones explored here are worth developing and using. Their strengths lie in (i) synthesis of the problem in a logical and transparent framework, (ii) helping scientists to deliberate how to apply their science to
Systematic design of membership functions for fuzzy-logic control: A case study on one-stage partial nitritation/anammox treatment systems.
Boiocchi, Riccardo; Gernaey, Krist V; Sin, Gürkan
A methodology is developed to systematically design the membership functions of fuzzy-logic controllers for multivariable systems. The methodology consists of a systematic derivation of the critical points of the membership functions as a function of predefined control objectives. Several constrained optimization problems corresponding to different qualitative operation states of the system are defined and solved to identify, in a consistent manner, the critical points of the membership functions for the input variables. The consistently identified critical points, together with the linguistic rules, determine the long term reachability of the control objectives by the fuzzy logic controller. The methodology is highlighted using a single-stage side-stream partial nitritation/Anammox reactor as a case study. As a result, a new fuzzy-logic controller for high and stable total nitrogen removal efficiency is designed. Rigorous simulations are carried out to evaluate and benchmark the performance of the controller. The results demonstrate that the novel control strategy is capable of rejecting the long-term influent disturbances, and can achieve a stable and high TN removal efficiency. Additionally, the controller was tested, and showed robustness, against measurement noise levels typical for wastewater sensors. A feedforward-feedback configuration using the present controller would give even better performance. In comparison, a previously developed fuzzy-logic controller using merely expert and intuitive knowledge performed worse. This proved the importance of using a systematic methodology for the derivation of the membership functions for multivariable systems. These results are promising for future applications of the controller in real full-scale plants. Furthermore, the methodology can be used as a tool to help systematically design fuzzy logic control applications for other biological processes. PMID:27390035
High quality mask storage in an advanced Logic-Fab
NASA Astrophysics Data System (ADS)
Jähnert, Carmen; Fritsche, Silvio
High efficient mask logistics as well as safe and high quality mask storage are essential requirements within an advanced lithography area of a modern logic waferfab. Fast operational availability of the required masks at the exposure tool with excellent mask condition requires a safe mask handling, safeguarding of high mask quality over the whole mask usage time without any quality degradation and an intelligent mask logistics. One big challenge is the prevention of haze on high advanced phase shift masks used in a high volume production line for some thousands of 248nm or 193nm exposures. In 2008 Infineon Dresden qualified a customer specific developed semi-bare mask storage system from DMSDynamic Micro Systems in combination with a high advanced mask handling and an interconnected complex logistic system. This high-capacity mask storage system DMS M1900.22 for more than 3000 masks with fully automated mask and box handling as well as full-blown XCDA purge has been developed and adapted to the Infineon Lithotoollandscape using Nikon and SMIF reticle cases. Advanced features for ESD safety and mask security, mask tracking via RFID and interactions with the exposure tools were developed and implemented. The stocker is remote controlled by the iCADA-RSM system, ordering of the requested mask directly from the affected exposure tool allows fast access. This paper discusses the advantages and challenges for this approach as well as the practical experience gained during the implementation of the new system which improves the fab performance with respect to mask quality, security and throughput. Especially the realization of an extremely low and stable humidity level in addition with a well controlled air flow at each mask surface, preventing masks from haze degradation and particle contamination, turns out to be a notable technical achievement. The longterm stability of haze critical masks has been improved significantly. Relevant environmental parameters like
Fuzzy-logic assessment of failure hazard in pipelines due to mining activity
NASA Astrophysics Data System (ADS)
Malinowska, A. A.; Hejmanowski, R.
The present research is aimed at a critical analysis of a method presently used for evaluating failure hazard in linear objects in mining areas. A fuzzy model of failure hazard of a linear object was created on the basis of the experience gathered so far. The rules of Mamdani fuzzy model have been used in the analyses. Finally the scaled model was integrated with a Geographic Information System (GIS), which was used to evaluate failure hazard in a water pipeline in a mining area.
Innovative Bimolecular-Based Advanced Logic Operations: A Prime Discriminator and An Odd Parity Checker.
Zhou, Chunyang; Liu, Dali; Dong, Shaojun
Herein, a novel logic operation of prime discriminator is first performed for the function of identifying the prime numbers from natural numbers less than 10. The prime discriminator logic operation is developed by DNA hybridizations and the conjugation of graphene oxide and single-stranded DNA as a reacting platform. On the basis of the similar reaction principle, an odd parity checker is also developed. The odd parity checker logic operation can identify the even numbers and odd numbers from natural numbers less than 10. Such advanced logic operations with digital recognition ability can provide a new field of vision toward prototypical DNA-based logic operations and promote the development of advanced logic circuits. PMID:27459592
Evaluation of Fuzzy-Logic Framework for Spatial Statistics Preserving Methods for Estimation of Missing Precipitation Data
NASA Astrophysics Data System (ADS)
El Sharif, H.; Teegavarapu, R. S.
Spatial interpolation methods used for estimation of missing precipitation data at a site seldom check for their ability to preserve site and regional statistics. Such statistics are primarily defined by spatial correlations and other site-to-site statistics in a region. Preservation of site and regional statistics represents a means of assessing the validity of missing precipitation estimates at a site. This study evaluates the efficacy of a fuzzy-logic methodology for infilling missing historical daily precipitation data in preserving site and regional statistics. Rain gauge sites in the state of Kentucky, USA, are used as a case study for evaluation of this newly proposed method in comparison to traditional data infilling techniques. Several error and performance measures will be used to evaluate the methods and trade-offs in accuracy of estimation and preservation of site and regional statistics.
Dynamic Event Tree advancements and control logic improvements
Alfonsi, Andrea; Rabiti, Cristian; Mandelli, Diego; Sen, Ramazan Sonat; Cogliati, Joshua Joseph
The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment, uncertainty quantification, data mining analysis and optimization studies. RAVEN is currently equipped with three different sampling categories: Forward samplers (Monte Carlo, Latin Hyper Cube, Stratified, Grid Sampler, Factorials, etc.), Adaptive Samplers (Limit Surface search, Adaptive Polynomial Chaos, etc.) and Dynamic Event Tree (DET) samplers (Deterministic and Adaptive Dynamic Event Trees). The main subject of this document is to report the activities that have been done in order to: start the migration of the RAVEN/RELAP-7 control logic system into MOOSE, and develop advanced dynamic sampling capabilities based on the Dynamic Event Tree approach. In order to provide to all MOOSE-based applications a control logic capability, in this Fiscal Year an initial migration activity has been initiated, moving the control logic system, designed for RELAP-7 by the RAVEN team, into the MOOSE framework. In this document, a brief explanation of what has been done is going to be reported. The second and most important subject of this report is about the development of a Dynamic Event Tree (DET) sampler named "Hybrid Dynamic Event Tree" (HDET) and its Adaptive variant "Adaptive Hybrid Dynamic Event Tree" (AHDET). As other authors have already reported, among the different types of uncertainties, it is possible to discern two principle types: aleatory and epistemic uncertainties. The classical Dynamic Event Tree is in charge of treating the first class (aleatory) uncertainties; the dependence of the probabilistic risk assessment and analysis on the epistemic uncertainties are treated by an initial Monte Carlo sampling (MCDET). From each Monte Carlo sample, a DET analysis is run (in total, N trees). The Monte Carlo employs a pre-sampling of the
Usage Intention Framework Model: A Fuzzy Logic Interpretation of the Classical Utaut Model
ERIC Educational Resources Information Center
Sandaire, Johnny
A fuzzy conjoint analysis (FCA: Turksen, 1992) model for enhancing management decision in the technology adoption domain was implemented as an extension to the UTAUT model (Venkatesh, Morris, Davis, & Davis, 2003). Additionally, a UTAUT-based Usage Intention Framework Model (UIFM) introduced a closed-loop feedback system. The empirical evidence…
The Use of a Predictive Habitat Model and a Fuzzy Logic Approach for Marine Management and Planning
Hattab, Tarek; Ben Rais Lasram, Frida; Albouy, Camille; Sammari, Chérif; Romdhane, Mohamed Salah; Cury, Philippe; Leprieur, Fabien; Le Loc'h, François
Bottom trawl survey data are commonly used as a sampling technique to assess the spatial distribution of commercial species. However, this sampling technique does not always correctly detect a species even when it is present, and this can create significant limitations when fitting species distribution models. In this study, we aim to test the relevance of a mixed methodological approach that combines presence-only and presence-absence distribution models. We illustrate this approach using bottom trawl survey data to model the spatial distributions of 27 commercially targeted marine species. We use an environmentally- and geographically-weighted method to simulate pseudo-absence data. The species distributions are modelled using regression kriging, a technique that explicitly incorporates spatial dependence into predictions. Model outputs are then used to identify areas that met the conservation targets for the deployment of artificial anti-trawling reefs. To achieve this, we propose the use of a fuzzy logic framework that accounts for the uncertainty associated with different model predictions. For each species, the predictive accuracy of the model is classified as 'high'. A better result is observed when a large number of occurrences are used to develop the model. The map resulting from the fuzzy overlay shows that three main areas have a high level of agreement with the conservation criteria. These results align with expert opinion, confirming the relevance of the proposed methodology in this study. PMID:24146867
Walking motion generation, synthesis, and control for biped robot by using PGRL, LPI, and fuzzy logic.
Li, Tzuu-Hseng S; Su, Yu-Te; Lai, Shao-Wei; Hu, Jhen-Jia
This paper proposes the implementation of fuzzy motion control based on reinforcement learning (RL) and Lagrange polynomial interpolation (LPI) for gait synthesis of biped robots. First, the procedure of a walking gait is redefined into three states, and the parameters of this designed walking gait are determined. Then, the machine learning approach applied to adjusting the walking parameters is policy gradient RL (PGRL), which can execute real-time performance and directly modify the policy without calculating the dynamic function. Given a parameterized walking motion designed for biped robots, the PGRL algorithm automatically searches the set of possible parameters and finds the fastest possible walking motion. The reward function mainly considered is first the walking speed, which can be estimated from the vision system. However, the experiment illustrates that there are some stability problems in this kind of learning process. To solve these problems, the desired zero moment point trajectory is added to the reward function. The results show that the robot not only has more stable walking but also increases its walking speed after learning. This is more effective and attractive than manual trial-and-error tuning. LPI, moreover, is employed to transform the existing motions to the motion which has a revised angle determined by the fuzzy motion controller. Then, the biped robot can continuously walk in any desired direction through this fuzzy motion control. Finally, the fuzzy-based gait synthesis control is demonstrated by tasks and point- and line-target tracking. The experiments show the feasibility and effectiveness of gait learning with PGRL and the practicability of the proposed fuzzy motion control scheme. PMID:21095871
Improving the performance of water balance equation using fuzzy logic approach
NASA Astrophysics Data System (ADS)
Khazaei, Bahram; Hosseini, Seyed Mahmood
It is a common practice to conduct the water budget or water balance analysis in a given area within a specified time in order to investigate the balance between the inputs and outputs of the water system. Such an analysis can be used for water management and water allocation in a designated study area. Due to appearance of an error in water balance equation because of difficulty in accurate estimation of its individual components, the main objective of the current paper was to apply a set of fuzzy coefficients to the components of the water balance equation in order to reduce this error. The fuzzy coefficients reflect the uncertainty and imprecision in evaluating each component, and minimize the overall error of the water balance equation. These coefficients are adjusted by an error minimization procedure, based on fuzzy regression concepts and using available recorded data for a given study area within a specified time scale. The adjusted coefficients can effectively estimate the water balance components in the future. In this study, four different models, representing different types of fuzzy coefficients, were considered and used for annual water balance of Azghand catchment in Khorasan Razavi Province, Iran as a case study. Analysis of results showed that all models were effective in reducing water balance error in Azghand catchment. The best model reduced the error up to 79% in terms of mean absolute error compared with error in water balance equation when conventional (with no correction coefficients) water balance analysis was conducted. Moreover, the results indicated that the performance of the proposed fuzzy models was not significantly sensitive to selection of confidence level in data (h) and improved slightly as h increased.
Multi-objective design of fuzzy logic controller in supply chain
NASA Astrophysics Data System (ADS)
Ghane, Mahdi; Tarokh, Mohammad Jafar
Unlike commonly used methods, in this paper, we have introduced a new approach for designing fuzzy controllers. In this approach, we have simultaneously optimized both objective functions of a supply chain over a two-dimensional space. Then, we have obtained a spectrum of optimized points, each of which represents a set of optimal parameters which can be chosen by the manager according to the importance of objective functions. Our used supply chain model is a member of inventory and order-based production control system family, a generalization of the periodic review which is termed `Order-Up-To policy.' An auto rule maker, based on non-dominated sorting genetic algorithm-II, has been applied to the experimental initial fuzzy rules. According to performance measurement, our results indicate the efficiency of the proposed approach.
Estimating Gas and Rock Outburst Risk on the Basis of Knowledge and Experience - The Expert System Based on Fuzzy Logic
NASA Astrophysics Data System (ADS)
Skoczylas, Norbert
The Author endeavored to consult some of the Polish experts who deal with assessing and preventing outburst hazards as to their knowledge and experience. On the basis of this knowledge, an expert system, based on fuzzy logic, was created. The system allows automatic assessment of outburst hazard. The work was completed in two stages. The first stage involved researching relevant sources and rules concerning outburst hazard, and, subsequently, determining a number of parameters measured or observed in the mining industry that are potentially connected with the outburst phenomenon and can be useful when estimating outburst hazard. Then, the Author contacted selected experts who are actively involved in preventing outburst hazard, both in the industry and science field. The experts were anonymously surveyed, which made it possible to select the parameters which are the most essential in assessing outburst hazard. The second stage involved gaining knowledge from the experts by means of a questionnaire-interview. Subjective opinions on estimating outburst hazard on the basis of the parameters selected during the first stage were then systematized using the structures typical of the expert system based on fuzzy logic. Autor współpracował z polskimi ekspertami związanymi z badaniami i zwalczaniem zagrożenia wyrzutowego. W wyniku współpracy pozyskał ich wiedzę i doświadczenie, na bazie której stworzył system ekspercki, oparty na logice rozmytej. System umożliwia automatyczną ocenę zagrożenia wyrzutowego. Prace przebiegały w dwóch etapach. Pierwszy etap polegał na określeniu istotnych parametrów mierzonych, bądź obserwowanych w górnictwie, które mają silny związek ze stanem zagrożenia wyrzutowego. Następnie wybrana grupa ekspertów dokonała oceny stanu zagrożenia wyrzutowego, bazując na parametrach które w pierwszym etapie uznane zostały za najistotniejsze. Pozwoliło to zbudować system ekspercki na bazie logiki rozmytej, który okre
Fundamentals of a fuzzy-logic-based generalized theory of stability.
Aliev, Rafik A; Pedrycz, Witold
Stability is one of the fundamental concepts of complex dynamical systems including physical, economical, socioeconomical, and technical systems. In classical terms, the notion of stability inherently associates with any dynamical system and determines whether a system under consideration reaches equilibrium after being exposed to disturbances. Predominantly, this concept comes with a binary (Boolean) quantification (viz., we either quantify that systems are stable or not stable). While in some cases, this definition is well justifiable, with the growing complexity and diversity of systems one could seriously question the Boolean nature of the definition and its underlying semantics. This becomes predominantly visible in human-oriented quantification of stability in which we commonly encounter statements quantifying stability through some linguistic terms such as, e.g., absolutely unstable, highly unstable, ..., absolutely stable, and alike. To formulate human-oriented definitions of stability, we may resort ourselves to the use of a so-called Precisiated Natural Language, which comes as a subset of natural language and one of whose functions is redefining existing concepts, such as stability, optimality, and alike. Being prompted by the discrepancy of the definition of stability and the Boolean character of the concept itself, in this paper, we introduce and develop a Generalized Theory of Stability (GTS) for analysis of complex dynamical systems described by fuzzy differential equations. Different human-centric definitions of stability of dynamical systems are introduced. We also discuss and contrast several fundamental concepts of fuzzy stability, namely, fuzzy stability of systems, binary stability of fuzzy system, and binary stability of systems by showing that all of them arise as special cases of the proposed GTS. The introduced definitions offer an important ability to quantify the concept of stability using some continuous quantification (that is through
Coalition possibility of riparian countries via game theory and fuzzy logic models
NASA Astrophysics Data System (ADS)
Kucukmehmetoglu, Mehmet; ŞEn, Zekai; Ã-Zger, Mehmet
In many respects, water resources allocation problems have fuzzy characteristics owing to uncertainty and imprecision not only as numerical data but also as linguistic data, in addition to the political nature of resource allocation. All classical techniques require numerical data and cannot treat linguistic data, but fuzzy inference systems (FIS) can deal with both types of data. Hence, it is suitable to reflect the dynamic nature of country benefits, reflective variations in the main drainage and subdrainage basin flows, and various system parameters in search of a coalition among a few riparian countries. The size of core, which is generated from a series of linear programming (LP) optimization models and game theory concepts, shows the potential extra benefit advantage of being in grand coalition to all parties, but it does not provide a sufficient condition to assemble a robust coalition unless an agreeable allocation scheme or principle is provided. In this respect, there is a fuzzy link between being in the grand coalition and percentage share received from a core. Hence, a proper FIS is proposed, and the necessary steps are developed with the Shapley value and the core as game theory methods, which provide a methodological base in the evaluation of possible allocation schemes among the parties. The application of the methodology is presented to search for coalition possibilities among the riparian countries: Turkey, Syria, and Iraq. Basic results are generated over the application of the Inter-Temporal Euphrates and Tigris River Basin Model, which is an optimization model for the allocation of scarce water resources considering agricultural and urban uses, energy generation, and conveyance costs in the Euphrates-Tigris basin.
On the use of fuzzy logic assessment for high consequence implementation risk analysis
Spray, S.; Cooper, A.; Bennett, R.
``High consequence`` operations are systems, structures, and/or strategies for which it is crucial to provide assured protection against some potential catastrophe or catastrophes. The word ``catastrophe`` implies a significant loss of a resource (e.g., money, lives, health, environment, national security, etc.). The implementation of operations that are to be as catastrophe-free as possible must incorporate a very high level of protection. Unfortunately, real world limitations on available resources, mainly money and time, preclude absolute protection. For this reason, conventional ``risk analysis`` focuses on ``cost-effective`` protection, demonstrating through analysis that the benefits of any protective measures chosen outweigh their cost. This is a ``crisp`` one-parameter (usually monetary) comparison. A major problem with this approach, especially for high consequence operations, is that it may not be possible to accurately determine quantitative ``costs,`` and furthermore, the costs may not be accurately quantifiable. Similarly, it may not be possible to accurately determine or to quantify the benefits of protection in high consequence operations. These weaknesses are addressed in this paper by introducing multiple parameters instead of a single monetary measure both for costs of implementing protective measures and their benefits. In addition, a fuzzy-algebra comparison based on fuzzy number theory is introduced as a tool in providing cost/benefit tradeoff depiction, with the incorporation of measures of the uncertainty that necessarily exists in the input information. The result allows a more informative comparison to be made through use of fuzzy results, especially at the extreme bounds of the uncertainty.
Assessment and prediction of air quality using fuzzy logic and autoregressive models
NASA Astrophysics Data System (ADS)
Carbajal-Hernández, José Juan; Sánchez-Fernández, Luis P.; Carrasco-Ochoa, Jesús A.; Martínez-Trinidad, José Fco.
In recent years, artificial intelligence methods have been used for the treatment of environmental problems. This work, presents two models for assessment and prediction of air quality. First, we develop a new computational model for air quality assessment in order to evaluate toxic compounds that can harm sensitive people in urban areas, affecting their normal activities. In this model we propose to use a Sigma operator to statistically asses air quality parameters using their historical data information and determining their negative impact in air quality based on toxicity limits, frequency average and deviations of toxicological tests. We also introduce a fuzzy inference system to perform parameter classification using a reasoning process and integrating them in an air quality index describing the pollution levels in five stages: excellent, good, regular, bad and danger, respectively. The second model proposed in this work predicts air quality concentrations using an autoregressive model, providing a predicted air quality index based on the fuzzy inference system previously developed. Using data from Mexico City Atmospheric Monitoring System, we perform a comparison among air quality indices developed for environmental agencies and similar models. Our results show that our models are an appropriate tool for assessing site pollution and for providing guidance to improve contingency actions in urban areas.
Improved sensitivity in the diagnosis of gastro-intestinal tumors by fuzzy logic-based tumor marker profiles including the tumor M2-PK.
Schneider, Joachim; Bitterlich, Norman; Schulze, Guntram
The aim of this study was to improve diagnostic efficiency in the detection of gastro-intestinal cancers by using fuzzy logic modeling in combination with a tumor marker panel (CEA, CA72-4, CA19-9) including Tumor M2-PK. In this prospective study histologically confirmed colorectal (n=247), esophageal (n=86) and gastric cancer (n=122) patients were investigated and compared to control (n=53) persons without any malignant diseases. Tumor M2-PK was measured in plasma with an ELISA (ScheBoBiotech, Germany); all other markers were measured in sera (Roche, Germany). At 95% specificity, tumor detection was possible by the best single marker in colorectal cancer patients in 48% (Tumor M2-PK), in gastric cancers in 61% (CA72-4) and in esophageal cancers in 56% (Tumor M2-PK). A fuzzy logic rule-based system employing a tumor marker panel increased sensitivity significantly in colorectal cancers (p<0. 001) to 63% (Tumor M2-PK and CEA), in gastric cancers (p<0.001) to 81% (Tumor M2-PK and CA 72-4) and in esophageal cancers (p<0.02) to 74% (Tumor M2-PK and CA72-4). Adding a third marker further improved the sensitivity only marginally. Fuzzy logic analysis has proven to be more powerful than measurement of single markers alone or combinations using multiple logistic regression analysis of the markers. Therefore, with the fuzzy logic method and a tumor marker panel (including Tumor M2-PK), a new diagnostic tool for the detection of gastro-intestinal cancers is available. PMID:16033052
Zadeh, L.A.
The applicability of conventional mathematical analysis (based on the combination of two-valued logic and probability theory) to problems in which human judgment, perception, or emotions play significant roles is considered theoretically. It is shown that dispositional logic, a branch of fuzzy logic, has particular relevance to the common-sense reasoning typical of human decision-making. The concepts of dispositionality and usuality are defined analytically, and a dispositional conjunctive rule and dispositional modus ponens are derived. 7 references.
NASA Technical Reports Server (NTRS)
Le Balleur, J. C.
The applicability of conventional mathematical analysis (based on the combination of two-valued logic and probability theory) to problems in which human judgment, perception, or emotions play significant roles is considered theoretically. It is shown that dispositional logic, a branch of fuzzy logic, has particular relevance to the common-sense reasoning typical of human decision-making. The concepts of dispositionality and usuality are defined analytically, and a dispositional conjunctive rule and dispositional modus ponens are derived.
ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster.
Volna, Eva; Kotyrba, Martin; Habiballa, Hashim
The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series. PMID:26221620
Retina identification based on the pattern of blood vessels using fuzzy logic
NASA Astrophysics Data System (ADS)
Barkhoda, Wafa; Akhlaqian, Fardin; Amiri, Mehran Deljavan; Nouroozzadeh, Mohammad Sadeq
This article proposed a novel human identification method based on retinal images. The proposed system composed of two main parts, feature extraction component and decision-making component. In feature extraction component, first blood vessels extracted and then they have been thinned by a morphological algorithm. Then, two feature vectors are constructed for each image, by utilizing angular and radial partitioning. In previous studies, Manhattan distance has been used as similarity measure between images. In this article, a fuzzy system with Manhattan distances of two feature vectors as input and similarity measure as output has been added to decision-making component. Simulations show that this system is about 99.75% accurate which make it superior to a great extent versus previous studies. In addition to high accuracy rate, rotation invariance and low computational overhead are other advantages of the proposed systems that make it ideal for real-time systems.
Groundwater vulnerability and risk mapping using GIS, modeling and a fuzzy logic tool.
Nobre, R C M; Rotunno Filho, O C; Mansur, W J; Nobre, M M M; Cosenza, C A N
A groundwater vulnerability and risk mapping assessment, based on a source-pathway-receptor approach, is presented for an urban coastal aquifer in northeastern Brazil. A modified version of the DRASTIC methodology was used to map the intrinsic and specific groundwater vulnerability of a 292 km(2) study area. A fuzzy hierarchy methodology was adopted to evaluate the potential contaminant source index, including diffuse and point sources. Numerical modeling was performed for delineation of well capture zones, using MODFLOW and MODPATH. The integration of these elements provided the mechanism to assess groundwater pollution risks and identify areas that must be prioritized in terms of groundwater monitoring and restriction on use. A groundwater quality index based on nitrate and chloride concentrations was calculated, which had a positive correlation with the specific vulnerability index. PMID:17728007
Adaptive neuro-fuzzy logic analysis based on myoelectric signals for multifunction prosthesis control.
Favieiro, Gabriela W; Balbinot, Alexandre
The myoelectric signal is a sign of control of the human body that contains the information of the user's intent to contract a muscle and, therefore, make a move. Studies shows that the Amputees are able to generate standardized myoelectric signals repeatedly before of the intention to perform a certain movement. This paper presents a study that investigates the use of forearm surface electromyography (sEMG) signals for classification of five distinguish movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an adaptive neuro-fuzzy inference system (ANFIS) to process signal features to recognize performed movements. The average accuracy reached for the classification of five motion classes was 86-98% for three subjects. PMID:22256169
Using GIS and Fuzzy Logic for Wastewater Treatment Processes Site Selection: The Case of Rodopi Prefecture
NASA Astrophysics Data System (ADS)
Anagnostopoulos, K. P.; Vavatsikos, A. P.
We perform a GIS-based land-use suitability analysis aiming to identify appropriate locations for implementing natural systems for the wastewater treatment in the Rodopi Prefecture-Northeast Greece. The analysis, implemented in a Spatial Decision Support System that combines GIS and the fuzzy extension of the Analytic Hierarchy Process (FAHP), is carried out in three steps. First, threshold values for the criterion maps are decided and the mask of the candidate locations for the final analysis is constructed. Second, the decision criteria are selected and FAHP is used in order to derive weights of preference among them. Finally, the criterion maps are weighted and standardized in order to calculate the overall suitability index for the final classification.
Supporting capacity sharing in the cloud manufacturing environment based on game theory and fuzzy logic
NASA Astrophysics Data System (ADS)
Argoneto, Pierluigi; Renna, Paolo
This paper proposes a Framework for Capacity Sharing in Cloud Manufacturing (FCSCM) able to support the capacity sharing issue among independent firms. The success of geographical distributed plants depends strongly on the use of opportune tools to integrate their resources and demand forecast in order to gather a specific production objective. The framework proposed is based on two different tools: a cooperative game algorithm, based on the Gale-Shapley model, and a fuzzy engine. The capacity allocation policy takes into account the utility functions of the involved firms. It is shown how the capacity allocation policy proposed induces all firms to report truthfully their information about their requirements. A discrete event simulation environment has been developed to test the proposed FCSCM. The numerical results show the drastic reduction of unsatisfied capacity obtained by the model of cooperation implemented in this work.
ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster
Volna, Eva; Kotyrba, Martin; Habiballa, Hashim
The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series. PMID:26221620
Performance Analysis of Extracted Rule-Base Multivariable Type-2 Self-Organizing Fuzzy Logic Controller Applied to Anesthesia
Fan, Shou-Zen; Shieh, Jiann-Shing
We compare type-1 and type-2 self-organizing fuzzy logic controller (SOFLC) using expert initialized and pretrained extracted rule-bases applied to automatic control of anaesthesia during surgery. We perform experimental simulations using a nonfixed patient model and signal noise to account for environmental and patient drug interaction uncertainties. The simulations evaluate the performance of the SOFLCs in their ability to control anesthetic delivery rates for maintaining desired physiological set points for muscle relaxation and blood pressure during a multistage surgical procedure. The performances of the SOFLCs are evaluated by measuring the steady state errors and control stabilities which indicate the accuracy and precision of control task. Two sets of comparisons based on using expert derived and extracted rule-bases are implemented as Wilcoxon signed-rank tests. Results indicate that type-2 SOFLCs outperform type-1 SOFLC while handling the various sources of uncertainties. SOFLCs using the extracted rules are also shown to outperform those using expert derived rules in terms of improved control stability. PMID:25587533
On-line equalization for lithium-ion battery packs based on charging cell voltages: Part 2. Fuzzy logic equalization
NASA Astrophysics Data System (ADS)
Zheng, Yuejiu; Ouyang, Minggao; Lu, Languang; Li, Jianqiu; Han, Xuebing; Xu, Liangfei
In the first part of this work, we propose dissipative cell equalization (DCE) algorithm based on remaining charging capacity estimation (RCCE) and establish a pack model with 8 cells in series. The results show that RCCE-DCE algorithm is suitable for on-line equalization in electric vehicles (EVs) and no over-equalization happens. However, 1% pack capacity difference from the DCE theoretical pack capacity is observed with RCCE-DCE algorithm. Therefore, as the second part of the series, we propose fuzzy logic (FL) DCE algorithm based on charging cell voltage curves (CCVCs). Cell capacities and SOCs are fuzzily identified in FL-DCE algorithm by comparing cell voltages at the beginning and end of charging. Adaptive FL-DCE is further improved to prevent over-equalization and maintain the equalization capability. The simulation results show that pack capacity difference from the DCE theoretical pack capacity with the adaptive FL-DCE is smaller than that with RCCE-DCE algorithm, and the duration of the infant stage is also shorter. The proposed adaptive FL-DCE is suitable for on-line equalization in EVs and well prevents over-equalization.
Optimization of Surface Roughness Parameters of Al-6351 Alloy in EDC Process: A Taguchi Coupled Fuzzy Logic Approach
NASA Astrophysics Data System (ADS)
Kar, Siddhartha; Chakraborty, Sujoy; Dey, Vidyut; Ghosh, Subrata Kumar
This paper investigates the application of Taguchi method with fuzzy logic for multi objective optimization of roughness parameters in electro discharge coating process of Al-6351 alloy with powder metallurgical compacted SiC/Cu tool. A Taguchi L16 orthogonal array was employed to investigate the roughness parameters by varying tool parameters like composition and compaction load and electro discharge machining parameters like pulse-on time and peak current. Crucial roughness parameters like Centre line average roughness, Average maximum height of the profile and Mean spacing of local peaks of the profile were measured on the coated specimen. The signal to noise ratios were fuzzified to optimize the roughness parameters through a single comprehensive output measure (COM). Best COM obtained with lower values of compaction load, pulse-on time and current and 30:70 (SiC:Cu) composition of tool. Analysis of variance is carried out and a significant COM model is observed with peak current yielding highest contribution followed by pulse-on time, compaction load and composition. The deposited layer is characterised by X-Ray Diffraction analysis which confirmed the presence of tool materials on the work piece surface.
Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons.
Radojević, Miroslav; Smal, Ihor; Meijering, Erik
Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process. PMID:26701809
Determination of Reduced Number and Suitable Locations of Fuzzy Logic Controlled Braking Resistors for Transient Stability Enhancement
NASA Astrophysics Data System (ADS)
Ali, Mohd. Hasan; Murata, Toshiaki; Tamura, Junji
Braking resistor is known to be a very powerful tool for transient stability improvement in electric power systems. Usually, in a large power system braking resistors are placed at each generator terminal bus which requires a high installation as well as operation cost. Also, heavy computation is required for the controllers used for the switching of the resistors. From these viewpoints, this paper directs to the study of installation of reduced number of fuzzy logic controlled braking resistors at suitable locations for transient stability enhancement. Groups of coherent generators in the power system are determined. Then one braking resistor is installed in each of the coherent group and at each of the remaining generator terminal bus. Thus, the number of braking resistors is reduced and hence the installation and operation cost as well as computational burden for the controllers are minimized. The suitable location for the braking resistor in each coherent group of generators is determined according to the values of the transient stability index as calculated for a 3LG (Three-phase-to-ground) fault at the points near the generators of the coherent group without considering the braking resistors in the system. The effectiveness of the proposed method is demonstrated through EMTP simulations for the IEEJ West-10 machine model system.
Integrating GIS and fuzzy logic for urban solid waste management (a case study of Sanandaj city, Iran).
Lotfi, S; Habibi, K; Koohsari, M J
The city of Sanandaj with population of 340000 in 2003 produces 714 tons of solid waste per day. Waste dumping is the main method of solid waste disposal during the thirty past years. The conditions show that the present site is quite unsuitable because many principles and criteria for site selection are not considered. So in the near future, the severe environmental impacts will threats the health of Sanandaj's inhabitants. The research attempts to integrate fuzzy logic and GIS with the help of Remote Sensing in selecting the most suitable area for the waste disposal. In this study we georeferenced many data which ranges from physical to socio-economic to monitor the impacts of all different factors. Some of these factors are; distance from the legal limits of the city, distance from main road, airport, main urban utilities, other human settlements, surface water resources, wind direction, population density, gradient, fault line, vegetation cover and geology. The data and information has been integrated and then by using satellite images, three areas in the 20 km of north east of Sanandaj city were identified for the solid waste disposal. PMID:19090271
A fuzzy-logic based dual-purpose adaptive circuit for vibration control and energy harvesting using piezoelectric transducer
NASA Astrophysics Data System (ADS)
Liu, Zhe Peng; Li, Qing
Due to their two-way electromechanical coupling effect, piezoelectric transducers can be used to synthesize passive vibration control schemes, e.g., RLC circuit with the integration of inductance and resistance elements that is conceptually similar to damped vibration absorber. Meanwhile, the wide usage of wireless sensors has led to the recent enthusiasm of developing piezoelectric-based energy harvesting devices that can convert ambient vibratory energy into useful electrical energy. It can be shown that the integration of circuitry elements such as resistance and inductance can benefit the energy harvesting capability. Here we explore a dual-purpose circuit that can facilitate simultaneous vibration suppression and energy harvesting. It is worth noting that the goal of vibration suppression and the goal of energy harvesting may not always complement each other. That is, the maximization of vibration suppression doesn't necessarily lead to the maximization of energy harvesting, and vice versa. In this research, we develop a fuzzy-logic based algorithm to decide the proper selection of circuitry elements to balance between the two goals. As the circuitry elements can be online tuned, this research yields an adaptive circuitry concept for the effective manipulation of system energy and vibration suppression. Comprehensive analyses are carried out to demonstrate the concept and operation.
Fuzzy logic-based assessment for mapping potential infiltration areas in low-gradient watersheds.
Quiroz Londoño, Orlando Mauricio; Romanelli, Asunción; Lima, María Lourdes; Massone, Héctor Enrique; Martínez, Daniel Emilio
This paper gives an account of the design a logic-based approach for identifying potential infiltration areas in low-gradient watersheds based on remote sensing data. This methodological framework is applied in a sector of the Pampa Plain, Argentina, which has high level of agricultural activities and large demands for groundwater supplies. Potential infiltration sites are assessed as a function of two primary topics: hydrologic and soil conditions. This model shows the state of each evaluated subwatershed respecting to its potential contribution to infiltration mainly based on easily measurable and commonly used parameters: drainage density, geomorphologic units, soil media, land-cover, slope and aspect (slope orientation). Mapped outputs from the logic model displayed 42% very low-low, 16% moderate, 41% high-very high contribution to potential infiltration in the whole watershed. Subwatersheds in the upper and lower section were identified as areas with high to very high potential infiltration according to the following media features: low drainage density (<1.5 km/km(2)), arable land and pastures as the main land-cover categories, sandy clay loam to loam - clay loam soils and with the geomorphological units named poorly drained plain, channelized drainage plain and, dunes and beaches. PMID:27042973
Design of operating rules in complex water resources systems using historical records, expert criteria and fuzzy logic
NASA Astrophysics Data System (ADS)
Pulido-Velazquez, Manuel; Macian-Sorribes, Hector; María Benlliure-Moreno, Jose; Fullana-Montoro, Juan
Water resources systems in areas with a strong tradition in water use are complex to manage by the high amount of constraints that overlap in time and space, creating a complicated framework in which past, present and future collide between them. In addition, it is usual to find "hidden constraints" in system operations, which condition operation decisions being unnoticed by anyone but the river managers and users. Being aware of those hidden constraints requires usually years of experience and a degree of involvement in that system's management operations normally beyond the possibilities of technicians. However, their impact in the management decisions is strongly imprinted in the historical data records available. The purpose of this contribution is to present a methodology capable of assessing operating rules in complex water resources systems combining historical records and expert criteria. Both sources are coupled using fuzzy logic. The procedure stages are: 1) organize expert-technicians preliminary meetings to let the first explain how they manage the system; 2) set up a fuzzy rule-based system (FRB) structure according to the way the system is managed; 3) use the historical records available to estimate the inputs' fuzzy numbers, to assign preliminary output values to the FRB rules and to train and validate these rules; 4) organize expert-technician meetings to discuss the rule structure and the input's quantification, returning if required to the second stage; 5) once the FRB structure is accepted, its output values must be refined and completed with the aid of the experts by using meetings, workshops or surveys; 6) combine the FRB with a Decision Support System (DSS) to simulate the effect of those management decisions; 7) compare its results with the ones offered by the historical records and/or simulation or optimization models; and 8) discuss with the stakeholders the model performance returning, if it's required, to the fifth or the second stage
Using a hybrid genetic algorithm and fuzzy logic for metabolic modeling
Yen, J.; Lee, B.; Liao, J.C.
The identification of metabolic systems is a complex task due to the complexity of the system and limited knowledge about the model. Mathematical equations and ODE`s have been used to capture the structure of the model, and the conventional optimization techniques have been used to identify the parameters of the model. In general, however, a pure mathematical formulation of the model is difficult due to parametric uncertainty and incomplete knowledge of mechanisms. In this paper, we propose a modeling approach that (1) uses fuzzy rule-based model to augment algebraic enzyme models that are incomplete, and (2) uses a hybrid genetic algorithm to identify uncertain parameters in the model. The hybrid genetic algorithm (GA) integrates a GA with the simplex method in functional optimization to improve the GA`s convergence rate. We have applied this approach to modeling the rate of three enzyme reactions in E. coli central metabolism. The proposed modeling strategy allows (1) easy incorporation of qualitative insights into a pure mathematical model and (2) adaptive identification and optimization of key parameters to fit system behaviors observed in biochemical experiments.
Cocoa bean quality assessment by using hyperspectral images and fuzzy logic techniques
NASA Astrophysics Data System (ADS)
Soto, Juan; Granda, Guillermo; Prieto, Flavio; Ipanaque, William; Machacuay, Jorge
Nowadays, cocoa bean exportation from Piura-Peru is having a positive international market response due to their inherent high quality. Nevertheless, when using subjective techniques for quality assessment, such as the cut test, a wastefulness of grains is generated, additional to a restriction in the selection as well as improvement approaches in earlier stages for optimizing the quality. Thus, in an attempt to standardize the internal features analyzed by the cut test, for instance, crack formation and internal color changes during the fermentation, this research is submitted as an approach which aims to make use of hyperspectral images, with the purpose of having a quick and accurate analysis. Hyperspectral cube size was reduced by using Principal Component Analysis (PCA). The image generated by principal component PC1 provides enough information to clearly distinguish the internal cracks of the cocoa bean, since the zones where these cracks are, have a negative correlation with PC1. The features taken were processed through a fuzzy block, which is able to describe the cocoa bean quality. Three membership functions were defined in the output: unfermented, partly fermented and well fermented, by using trapezoidal-shaped and triangular-shaped functions. A total of twelve rules were propounded. Furthermore, the bisector method was chosen for the defuzzification. Begin the abstract two lines below author names and addresses.
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.
Type-1 and Type-2 Fuzzy Logic and Sliding-Mode Based Speed Control of Direct Torque and Flux Control Induction Motor Drives - A Comparative Study
NASA Astrophysics Data System (ADS)
Ramesh, Tejavathu; Panda, A. K.; Kumar, S. Shiva
In this research study, the performance of direct torque and flux control induction motor drive (IMD) is presented using five different speed control techniques. The performance of IMD mainly depends on the design of speed controller. The PI speed controller requires precise mathematical model, continuous and appropriate gain values. Therefore, adaptive control based speed controller is desirable to achieve high-performance drive. The sliding-mode speed controller (SMSC) is developed to achieve continuous control of motor speed and torque. Furthermore, the type-1 fuzzy logic speed controller (T1FLSC), type-1 fuzzy SMSC and a new type-2 fuzzy logic speed controller are designed to obtain high performance, dynamic tracking behaviour, speed accuracy and also robustness to parameter variations. The performance of each control technique has been tested for its robustness to parameter uncertainties and load disturbances. The detailed comparison of different control schemes are carried out in a MATALB/Simulink environment at different speed operating conditions, such as, forward and reversal motoring under no-load, load and sudden change in speed.
Type-2 fuzzy logic control based MRAS speed estimator for speed sensorless direct torque and flux control of an induction motor drive.
Ramesh, Tejavathu; Kumar Panda, Anup; Shiva Kumar, S
In this research study, a model reference adaptive system (MRAS) speed estimator for speed sensorless direct torque and flux control (DTFC) of an induction motor drive (IMD) using two adaptation mechanism schemes are proposed to replace the conventional proportional integral controller (PIC). The first adaptation mechanism scheme is based on Type-1 fuzzy logic controller (T1FLC), which is used to achieve high performance sensorless drive in both transient as well as steady state conditions. However, the Type-1 fuzzy sets are certain and unable to work effectively when higher degree of uncertainties presents in the system which can be caused by sudden change in speed or different load disturbances, process noise etc. Therefore, a new Type-2 fuzzy logic controller (T2FLC) based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties and improves the performance and also robust to various load torque and sudden change in speed conditions, respectively. The detailed performances of various adaptation mechanism schemes are carried out in a MATLAB/Simulink environment with a speed sensor and speed sensorless modes of operation when an IMD is operating under different operating conditions, such as, no-load, load and sudden change in speed, respectively. To validate the different control approaches, the system also implemented on real-time system and adequate results are reported for its validation. PMID:25887841
Uncertainty analysis of a combined Artificial Neural Network - Fuzzy logic - Kriging system for spatial and temporal simulation of Hydraulic Head.
NASA Astrophysics Data System (ADS)
Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.
The purpose of this study is to evaluate the uncertainty, using various methodologies, in a combined Artificial Neural Network (ANN) - Fuzzy logic - Kriging system, which can simulate spatially and temporally the hydraulic head in an aquifer. This system uses ANNs for the temporal prediction of hydraulic head in various locations, one ANN for every location with available data, and Kriging for the spatial interpolation of ANN's results. A fuzzy logic is used for the interconnection of these two methodologies. The full description of the initial system and its functionality can be found in Tapoglou et al. (2014). Two methodologies were used for the calculation of uncertainty for the implementation of the algorithm in a study area. First, the uncertainty of Kriging parameters was examined using a Bayesian bootstrap methodology. In this case the variogram is calculated first using the traditional methodology of Ordinary Kriging. Using the parameters derived and the covariance function of the model, the covariance matrix is constructed. A common method for testing a statistical model is the use of artificial data. Normal random numbers generation is the first step in this procedure and by multiplying them by the decomposed covariance matrix, correlated random numbers (sample set) can be calculated. These random values are then fitted into a variogram and the value in an unknown location is estimated using Kriging. The distribution of the simulated values using the Kriging of different correlated random values can be used in order to derive the prediction intervals of the process. In this study 500 variograms were constructed for every time step and prediction point, using the method described above, and their results are presented as the 95th and 5th percentile of the predictions. The second methodology involved the uncertainty of ANNs training. In this case, for all the data points 300 different trainings were implemented having different training datasets each time
Applying Fuzzy Logic for Learner Modeling and Decision Support in Online Learning Systems
ERIC Educational Resources Information Center
Al-Aubidy, Kasim M.
Advances in computers and multimedia technology have changed traditional methods for learning and skills training. Online learning continues to play a major success of any academic program. Such learning can personalize learning needs for students, it can provide an environment where virtual reality techniques are used to create interactive…
Comparison of Candidate Sites for installation of Landfill facility at Ignalina NPP Site Using Fuzzy Logic Approach
Poskas, P.; Kilda, R.; Poskas, G.
There is only one nuclear power plant in Lithuania - Ignalina NPP (Nuclear Power Plant). Two similar units with installed capacity of 1500 MW (each) were commissioned in 1983 and 1987 respectively. But the first Unit of Ignalina NPP was finally shutdown December 31, 2004, and second Unit is planned to be shutdown before 2010. Operational radioactive waste of different activities is generated at Ignalina NPP. After closure of INPP a waste from decommissioning should be managed also. According to Lithuanian regulatory requirements (1) the waste depending on the activity must be managed in different ways. In compliance with this Regulation very low-level radioactive waste (VLLW) could be disposed of in a Landfill facility. In such case very simple engineered barriers are required. A cap on the top of the repository is necessary from long-term safety point of view. Experience has shown that the effective and safe isolation of waste depends on the performance of the overall disposal system, which is formed by three major components: the site, the disposal facility and the waste form. The basic objective of the siting process is to select a suitable site for disposal and demonstrate that this site has characteristics which provide adequate isolation of radionuclides from the biosphere for desired periods of time. The methodology and results on evaluation and comparison of two candidate sites intended for construction of Landfill facility at Ignalina NPP site are presented in the paper. Criteria for comparison are based on the IAEA (International Atomic Energy Agency) recommendations (2). Modeling of the radionuclide releases has been performed using ISAM (Improving of Safety Assessment Methodologies for Near Surface Disposal facilities) methodology (3). For generalization of the information and elaboration of the recommendations Fuzzy Logic approach was used (4). (authors)
78 FR 49079 - Lease Modifications, Lease and Logical Mining Unit Diligence, Advance Royalty, Royalty Rates, and...
Federal Register 2010, 2011, 2012, 2013, 2014
... published in final form, following public comment, in the Federal Register on August 29, 1985 (50 FR 35145... August 30, 1982, must be revised to comply with the rules as modified as of August 30, 1982 (47 FR 33154..., et al. Lease Modifications, Lease and Logical Mining Unit Diligence, Advance Royalty, Royalty...
Global Single and Multiple Cloud Classification with a Fuzzy Logic Expert System
NASA Technical Reports Server (NTRS)
Welch, Ronald M.; Tovinkere, Vasanth; Titlow, James; Baum, Bryan A.
An unresolved problem in remote sensing concerns the analysis of satellite imagery containing both single and multiple cloud layers. While cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget, most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. Coakley (1983) used a spatial coherence method to determine whether a region contained more than one cloud layer. Baum et al. (1995) developed a scheme for detection and analysis of daytime multiple cloud layers using merged AVHRR (Advanced Very High Resolution Radiometer) and HIRS (High-resolution Infrared Radiometer Sounder) data collected during the First ISCCP Regional Experiment (FIRE) Cirrus 2 field campaign. Baum et al. (1995) explored the use of a cloud classification technique based on AVHRR data. This study examines the feasibility of applying the cloud classifier to global satellite imagery.
Development of an Expert System as a Diagnostic Support of Cervical Cancer in Atypical Glandular Cells, Based on Fuzzy Logics and Image Interpretation
Domínguez Hernández, Karem R.; Aguilar Lasserre, Alberto A.; Posada Gómez, Rubén; Palet Guzmán, José A.; González Sánchez, Blanca E.
Cervical cancer is the second largest cause of death among women worldwide. Nowadays, this disease is preventable and curable at low cost and low risk when an accurate diagnosis is done in due time, since it is the neoplasm with the highest prevention potential. This work describes the development of an expert system able to provide a diagnosis to cervical neoplasia (CN) precursor injuries through the integration of fuzzy logics and image interpretation techniques. The key contribution of this research focuses on atypical cases, specifically on atypical glandular cells (AGC). The expert system consists of 3 phases: (1) risk diagnosis which consists of the interpretation of a patient's clinical background and the risks for contracting CN according to specialists; (2) cytology images detection which consists of image interpretation (IM) and the Bethesda system for cytology interpretation, and (3) determination of cancer precursor injuries which consists of in retrieving the information from the prior phases and integrating the expert system by means of a fuzzy logics (FL) model. During the validation stage of the system, 21 already diagnosed cases were tested with a positive correlation in which 100% effectiveness was obtained. The main contribution of this work relies on the reduction of false positives and false negatives by providing a more accurate diagnosis for CN. PMID:23690881
Development of the on-line operator aid system OASYS using a rule-based expert system and fuzzy logic for nuclear power plants
Chang, S.H.; Kang, K.S.; Choi, S.S.; Kim, H.G.; Jeong, H.K.; Yi, C.U.
The On-line Operator Aid SYStem (OASYS) has been developed to support the operator`s decision making process and to ensure the safety of a nuclear power plant by providing operators with proper guidelines in a timely manner, according to the plant operation mode. The OASYS consists of four systems such as a signal validation and management system (SVMS), a plant monitoring system (PMS), an alarm filtering and diagnostic system (AFDS), and a dynamic emergency procedure tracking system (DEPTS). The SVMS and the PMS help operators to maintain a plant in a condition to withstand the adverse events during a normal operation condition. The AFDS covers the abnormal events until it exceeds the limit range of reactor trip signals, while after a reactor trip, the DEPTS aids operators with proper guidelines so as to shut down safely. The OASYS uses a rule-based expert system and fuzzy logic. The rule-based expert system is used to classify the predefined events and track the emergency operating procedures (EOPs) through data processing, and the fuzzy logic is used to generate the conceptual high-level alarms for the prognostic diagnosis and to evaluate the qualitative fuzzy criteria used in the EOPs. Evaluation results show that the OASYS is capable of diagnosing plant abnormal conditions and providing operators appropriate guidelines with fast response time and consistency. The proposed system is implemented on a SUN-4/75 workstation using C language and Quintus prolog language. Currently, the OASYS is installed in the real-time full scope simulator for validation.
Teaching and Learning in a Student Success Course: A Discussion Concerning the Development of the Internal Locus of Control Using Fuzzy Logic, TQM, and the Chaos Theory of Education.
ERIC Educational Resources Information Center
Loree, Tim; Stupka, Ed
An overview is provided of the relevant concepts from Total Quality Management (TQM), fuzzy logic, and the chaos theory of education in an effort to support the case for student success courses. First, the paper discusses student success courses, which emphasize helping students develop the skills needed to identify, create, and pursue an…
Simulation of reconfigurable multifunctional continuous logic devices as advanced components of the next generation high-performance MIMO-systems for the processing and interconnection
NASA Astrophysics Data System (ADS)
Krasilenko, Vladimir G.; Nikolskyy, Aleksandr I.; Lazarev, Alexander A.
We consider design and modeling of hardware realizations of reconfigurable multifunctional continuous logic devices (R MCL D) as advanced components of the next generation high-performance MIMO-systems for the processing and interconnection. The R MCL D realize function of two-valued and continuous logics with current inputs and current outputs on the basis of CMOS current mirrors and circuits which realize the limited difference functions. We show advantages of such elements consisting in encoding of variables by the photocurrent levels, that allows easily providing optical inputs (by photo-detectors (PD)) and optical outputs (by LED). The conception of construction of R MCL D consists in the use of a current mirrors realized on 1.5μm technology CMOS transistors. Presence of 55÷65 transistors, 1 PD and 1 LED makes the offered circuits quite compact and allows their integration in 1D and 2D arrays. In the presentation we consider the capabilities of the offered circuits, show the simulation results and possible prospects of application of the circuits in particular for time-pulse coding for multivalued, continuous, neuro-fuzzy and matrix logics. The simulation results of NOT, MIN, MAX, equivalence (EQ) and other functions, that implemented R MCL D, showed that the level of logical variables can change from 1 μA to 10 μA for low-power consumption variants. The base cell of the R MCL D have low power consumption <1mW and processing time about 1÷11μS at supply voltage 2.4÷3.3V. Modeling of such cells in OrCad is made.
Fuzzy mathematical techniques with applications
Kandel, A.
This text presents the basic concepts of fuzzy set theory within a context of real-world applications. The book is self-contained and can be used as a starting point for people interested in this fast growing field as well as by researchers looking for new application techniques. The section on applications includes: Manipulation of knowledge in expert systems; relational database structures; pattern clustering; analysis of transient behavior in digital systems; modeling of uncertainty and search trees. Contents: Fuzzy sets; Possibility theory and fuzzy quantification; Fuzzy functions; Fuzzy events and fuzzy statistics; Fuzzy relations; Fuzzy logics; Some applications; Bibliography.
Lithology identification of aquifers from geophysical well logs and fuzzy logic analysis: Shui-Lin Area, Taiwan
NASA Astrophysics Data System (ADS)
Hsieh, Bieng-Zih; Lewis, Charles; Lin, Zsay-Shing
The purpose of this study is to construct a fuzzy lithology system from well logs to identify formation lithology of a groundwater aquifer system in order to better apply conventional well logging interpretation in hydro-geologic studies because well log responses of aquifers are sometimes different from those of conventional oil and gas reservoirs. The input variables for this system are the gamma-ray log reading, the separation between the spherically focused resistivity and the deep very-enhanced resistivity curves, and the borehole compensated sonic log reading. The output variable is groundwater formation lithology. All linguistic variables are based on five linguistic terms with a trapezoidal membership function. In this study, 50 data sets are clustered into 40 training sets and 10 testing sets for constructing the fuzzy lithology system and validating the ability of system prediction, respectively. The rule-based database containing 12 fuzzy lithology rules is developed from the training data sets, and the rule strength is weighted. A Madani inference system and the bisector of area defuzzification method are used for fuzzy inference and defuzzification. The success of training performance and the prediction ability were both 90%, with the calculated correlation of training and testing equal to 0.925 and 0.928, respectively. Well logs and core data from a clastic aquifer (depths 100-198 m) in the Shui-Lin area of west-central Taiwan are used for testing the system's construction. Comparison of results from core analysis, well logging and the fuzzy lithology system indicates that even though the well logging method can easily define a permeable sand formation, distinguishing between silts and sands and determining grain size variation in sands is more subjective. These shortcomings can be improved by a fuzzy lithology system that is able to yield more objective decisions than some conventional methods of log interpretation.