A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems.

da Silva, I N; de Souza, A N; Bordon, M E

da Silva, I N; de Souza, A N; Bordon, M E.

Afiliação

da Silva IN; Department of Electrical Engineering, State University of São Paulo, Bauru, São Paulo 17033-360, Brazil. ivan@bauru.unesp.br

Int J Neural Syst ; 11(3): 281-6, 2001 Jun.

Article em En | MEDLINE | ID: mdl-11577380

RESUMO

A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.

Assuntos

Lógica Fuzzy; Redes Neurais de Computação; Dinâmica não Linear

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Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Redes Neurais de Computação / Lógica Fuzzy / Dinâmica não Linear Tipo de estudo: Prognostic_studies Idioma: En Revista: Int J Neural Syst Assunto da revista: ENGENHARIA BIOMEDICA / INFORMATICA MEDICA Ano de publicação: 2001 Tipo de documento: Article País de afiliação: Brasil País de publicação: Singapura

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