Framework for fuzzy neural networks

Noaki Imasaki; Jun-ichi Kiji; Masahiko Arai

doi:10.1117/12.140121

1 July 1992 Framework for fuzzy neural networks

Noaki Imasaki, Jun-ichi Kiji, Masahiko Arai

Proceedings Volume 1710, Science of Artificial Neural Networks; (1992) https://doi.org/10.1117/12.140121
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

Noaki Imasaki, Jun-ichi Kiji, and Masahiko Arai "Framework for fuzzy neural networks", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); https://doi.org/10.1117/12.140121

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