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Intelligent Learning Of Fuzzy Logic Controllers Via Neural Network And Genetic Algorithm

Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy setsand proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper develops methodologies to learn and optimize fuzzy logic controller parameters based on neural network and genetic algorithm. The strategies developed have been applied to control an inverted pendulum and results have been compared for three different fuzzy logic controllers developed with the help of iterative learning from operator experience, genetic algorithm and neural network. The results show that GeneticFuzzy and Neuro-Fuzzy approaches were able to learn rule base and identify membership function parameters accurately.