State Space Analysis and Design for Neural Networks
{'Code': '05020000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'IIS', 'LongName': 'Div Of Information & Intelligent Systems'}}
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The overall objective of this project is to develop a set of well          definedperformance indices and evaluation techniques to replace many            ad-hoc cut-and-try efforts currently used in neural network design.             This project is a studyof the state space of two neural networks:  the          asynchronous binary neural network (ABNN), and the Adaptive Resonance           Theoretic model (ART1).                                                              The project concentrates on the adaptation of these two                    structures to  realistic pattern environments, which are short-term             but not long term  stationary.  Specific goals include the analysis of          the dynamic behavior in  the model's state space, and the introduction          of structural and algorithmic  modifications that will improve their            performance as pattern recognizers.  An analytical approach based on            Markov chain theory, branching processes, and  optimal stopping rules           will be used.  The analysis will be verified through  simulations on a          serial computer as well as a truly parallel implementation on an                analog computer.
{'FirstName': 'Moshe', 'LastName': 'Kam', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Moshe Kam', 'EmailAddress': 'moshe.kam@njit.edu', 'NSF_ID': '000207753', 'StartDate': '07/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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[{'Code': '6840', 'Text': 'ROBOTICS'}, {'Code': '9120', 'Text': 'CROSS-DIRECTORATE PROGRAMS'}]
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