A novel approach based on recurrent neural networks applied to nonlinear systems optimization

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Data

2007-01-01

Autores

da Silva, Ivan Nunes
do Amaral, Wagner Caradori
de Arruda, Lucia Valeria

Título da Revista

ISSN da Revista

Título de Volume

Editor

Elsevier B.V.

Resumo

This paper presents an efficient approach based on recurrent neural network for solving nonlinear optimization. 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 that represent an optimal feasible solution. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network. (c) 2005 Elsevier B.V. All rights reserved.

Descrição

Palavras-chave

nonlinear optimization problems, recurrent neural networks, Hopfield networks, nonlinear programming

Como citar

Applied Mathematical Modelling. New York: Elsevier B.V., v. 31, n. 1, p. 78-92, 2007.