Design of Self-Learning Fuzzy System by GA Approach

Abstract

In this paper, an effective genetic algorithm (GA) approach is proposed for tuning the parameters of membership functions based on input-output pairs. By minimizing a quadratic measure of the error in the least-squares sense, the real-valued chromosomes of a population are evolved to get the best coefficients. Comparison to the well-known back-propagation algorithm for fuzzy logic system shows that both are powerful training algorithms, but much better performance is obtained with the proposed technique. Several numerical design examples are presented to demonstrate the efficiency and effectiveness of this proposed approach.