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Type: Artigo de periódico
Title: Neural approach for solving several types of optimization problems
Author: da Silva, IN
Amaral, WC
Arruda, LVR
Abstract: Neural networks consist of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation. This paper presents an architecture of recurrent neural net-works that can be used to solve several classes of optimization problems. More specifically, a modified Hopfield network is developed and its inter-nal parameters are computed explicitly using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points, which represent a solution of the problem considered. The problems that can be treated by the proposed approach include combinatorial optimiza-tion problems, dynamic programming problems, and nonlinear optimization problems.
Subject: recurrent neural networks
nonlinear optimization
dynamic programming
combinatorial optimization
Hopfield network
Country: EUA
Editor: Springer/plenum Publishers
Citation: Journal Of Optimization Theory And Applications. Springer/plenum Publishers, v. 128, n. 3, n. 563, n. 580, 2006.
Rights: fechado
Identifier DOI: 10.1007/s10957-006-9032-9
Date Issue: 2006
Appears in Collections:Unicamp - Artigos e Outros Documentos

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