Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/107391
Type: Artigo de evento
Title: Optimization Of Hierarchical Neural Fuzzy Models
Author: Campello Ricardo J.G.B.
Amaral Wagner C.
Abstract: Hierarchical fuzzy structures were introduced in previous work to deal with the dimensionality problem which is the main drawback to the application of neural networks and fuzzy models in the modeling and control of large-scale systems. In the present paper, the use of Radial Basis Function (RBF) networks connected in a hierarchical (cascade) fashion is investigated. The RBF networks are formulated as simplified fuzzy systems and the backpropagation equations for the optimization of the resulting hierarchical models are derived from this formulation. The optimization of the models using the conjugate gradient algorithm of Fletcher and Reeves is proposed and illustrated by means of a numerical example.
Editor: IEEE, Piscataway, NJ, United States
Rights: fechado
Identifier DOI: 
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-0033683801&partnerID=40&md5=f7c5cc3da2b3714d00bbf613df738254
Date Issue: 2000
Appears in Collections:Unicamp - Artigos e Outros Documentos

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