An Acquisition Method of Membership Functions and Fuzzy Reasoning Rules by Fuzzy Neural Network
Tatsuya Masuda, Toshihiko Ohta · IEEJ Transactions on Electronics Information and Systems · 1994
Formerly, we have proposed a method for acquiring fuzzy reasoning rules by a neural network. Since the neural network used in the method has a creative function of reasoning rules, so the only necessary rules to express the characteristics of controlled object are created in the network. The method, however, uses the fixed number of membership functions while learning. Thus we must determine beforehand the suitable number of them.In this paper, we propose a new acquiring method that extends the former method. Neural networks used in this method have a creative function of membership functions in addition to fuzzy reasoning rules. The result is that this method obtains the necessary and the minimum number of membership functions and fuzzy reasoning rules. Also we demonstrate the effectiveness of this method by appling it to two problems, i.e., an identification problem of nonlinear functions, and an obstacle avoidance problem of moving robot.