Automatic generation of membership function and fuzzy rule using inductive reasoning

Abstract

This paper discusses the automatic generation of membership function and fuzzy rule. The generation of them are accomplished by utilizing the essential characteristic of the inductive reasoning which derives a general consensus from the particular. The induction is performed by the entropy minimization principle which clusters most optimally the parameters corresponding to the output classes. The rule derivation also provide the average probability of each step of rule, which is no other than the rule weight. The generation scheme is illustrated for practical use.<>