Abstract
In this paper, we propose a method to use some kinds of membership functions (MSFs) efficiently to improve an optimization of fuzzy reasoning using a steepest descent method. In fuzzy reasoning, there are many problems, for example, rapid increase of the number of rules and large scale change of fuzzy system as the number of inputs increases. To overcome these problems, we add a technique of genetic algorithm to the optimization of fuzzy reasoning using the steepest descent method. In a technique of genetic algorithm, this new method can select some kinds of MSFs, delete some lengthy rules, and optimize MSFs. In addition, this new method can improve generalization ability as a result of selection of MSFs adapting to the model. The advantages of this new method are demonstrated by numerical examples involving function approximations.
