Abstract
This paper proposes fuzzy inference neural network (FiNN) as a framework for an incorporated system involving fuzzy theory and neural network theory. The FiNN is structured on a skeleton of specified fuzzy rules so that the FiNN can store the fuzzy rules smoothly. A FiNN system implements approximate inference from the fuzzy rules. There are three types for the structured parts, which are called `antecedent network,' or `conclusion network,' or `logic network.' Each structured part is a neural network component. Each neural network component executes an elementary function which is a part of an approximate inference procedure. The FiNN categorizes practical data by itself to generate learning samples for the conclusion networks. Membership functions in the antecedent networks are initialized by a priori knowledge, and modified by solving inverse problems of the logic network. A numerical example clarifies the applicability to the system identification.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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