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Author Figueiredo, M. ♦ Gomide, F.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1997
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Fuzzy neural networks ♦ Fuzzy systems ♦ Fuzzy reasoning ♦ Adaptive systems ♦ Decoding ♦ Knowledge representation ♦ Computer networks ♦ Computational modeling ♦ Analytical models ♦ Performance analysis
Abstract A new class of adaptive neural fuzzy networks for fuzzy modeling is introduced in this paper. It learns the essential parameters to model a fuzzy system such as fuzzy rules and membership functions. Fuzzy rules are easily encoded and decoded from its structure. These neural fuzzy networks also rigorously emulate fuzzy reasoning mechanisms. Because of their knowledge representation and computational features we can see the proposed system either as a neural fuzzy network or a fuzzy system. Thus, linguistic models are easily extracted from their structure. Simulation results and comparison analysis show that the proposed network has good performance considering two criteria: accuracy and number of rules derived.
Description Author affiliation: UNICAMP, Campinas, Brazil (Figueiredo, M.)
ISBN 0780337964
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1997-07-05
Publisher Place Barcelona
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 477.58 kB
Page Count 6
Starting Page 1567
Ending Page 1572


Source: IEEE Xplore Digital Library