A learning-automaton-based method for fuzzy inference system identification

Abstract

A learning method of fuzzy inference rules using learning automata is described. The tuning of the membership functions in the antecedent part and the real numbers in the consequent part of the inference rule can be stated as an optimization problem. Learning automata have been used for control and optimization purposes. The optimization algorithm is based on a hierarchical structure of stochastic automata with variable structures. Results related to numerical examples and fuzzy modelling are presented. Simulation results show the performance and the implementation simplicity of the proposed method.