Abstract
Some methods for acquiring fuzzy reasoning rules by neural network have been proposed. These methods, however, use multi-layered neural network which contains hidden units corresponding to all combinations of membership functions, so the number of hidden units (i.e., reasoning rules) increase exponentially as the number of input variables and their membership function increase. In order to solve this problems, in this paper we propose a new acquisition method of fuzzy reasoning rules. The neural network used in this method have a dynamic creative function of hidden units, so the only necessary rules to express the characteristics of controlled object are created in the network. The process of rule acquisition by this method resembles closely the process that a man makes fuzzy reasoning rules by trial and error. We demonstrate the effectiveness of this method by applying it two fuzzy control problems.
