Framework for fuzzy neural networks
N. Imasaki, Jun-ichi Kiji, Masahiko Arai · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
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.