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Proceedings Paper

Framework for fuzzy neural networks
Author(s): Noaki Imasaki; Jun-ichi Kiji; Masahiko Arai
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Paper 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.

Paper Details

Date Published: 1 July 1992
PDF: 12 pages
Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140121
Show Author Affiliations
Noaki Imasaki, Toshiba Systems and Software Engineering Lab. (Japan)
Jun-ichi Kiji, Toshiba Systems and Software Engineering Lab. (Japan)
Masahiko Arai, Toshiba Systems and Software Engineering Lab. (Japan)

Published in SPIE Proceedings Vol. 1710:
Science of Artificial Neural Networks
Dennis W. Ruck, Editor(s)

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