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Fuzzy-neural network model for effective control of profitability in a paper recycling plant

This research develops a fuzzy-neural network model, using AI technologies and applies the model for effective control of profitability in paper recycling to improve production accuracy, reliability, robustness and to maximize profit generated by an industry, despite varying cost of production upon which ultimately profit, in an industry depend. Recycling reduces greenhouse gas emissions, conserves the natural resources on Earth, and saves space in the landfills for future generations of people. A sustainable future requires a high degree of recycling. However, Recycling industries face serious economic problems that increase the cost of recycling. Fuzzy logic has emerged as a tool to deal with uncertain, imprecise, partial truth or qualitative decisionmaking problems, to achieve robustness, tractability, and low cost, but it cannot automatically acquire the rules it uses to make those decisions. Neural networks have the ability to learn, generalize and process large amount of numerical data, but they are not good at explaining how they reach their decisions. The hybrid fuzzy-neural system has the ability to overcome the limitations of individual technique and enhances their strengths to handle financial trading. In order to achieve our objective, a study of a knowledge based system for effective control of profitability in paper recycling is carried out. The Mamdani’s Max-Min technique is employed to infer data from the rules developed. This resulted in the establishment of some degrees of influence of input variables on the output. Fuzzy-Neural network model is developed using back propagation and supervised learning methods respectively. The outputs of Fuzzy logic serve as input to the neural network. To reinforce the proposed approach, we apply it to a case study performed on Paper recycling industry in Nigeria. A computer simulation is designed to assist the experimental decision for the best control action. The system is developed using MySQL, NetBeans, Java, MS Excel 2003, MatLab, etc. The obtained simulation and implementation fuzzy-neural results are investigated, compared and discussed.