FUZZY MODELING WITH GENETIC ALGORITHMS

Recent applications of fuzzy control have created an urgent demand for fuzzy modeling techniques. Several methods for identification of fuzz y models from numerical input-output samples have been proposed. Among them, Sugeno and Yasukawa's method [6], which employs fuzzy c-means c lustering, holds significant promises. This paper improves the method of Sugeno and Yasukawa. Identified fuzzy models are tuned at various s tages by means of genetic algorithms, i.e., the numbers of input varia bles and rules are reduced and membership function parameters are adju sted. The technique, when applied to a nonlinear system, demonstrates its efficiency in a comparison with the original method of Sugeno and Yasukama.