Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/100303
Type: Artigo de evento
Title: Adaptive Neuro Fuzzy Modeling
Author: Figueiredo M.
Gomide F.
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.
Editor: IEEE, Piscataway, NJ, United States
Rights: fechado
Identifier DOI: 
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-0030678919&partnerID=40&md5=e69f1e380ea641ac9b56cda7c13ba758
Date Issue: 1997
Appears in Collections:Unicamp - Artigos e Outros Documentos

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