Neural approach for solving several types of optimization problems
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Springer
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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.
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Keywords
recurrent neural networks, nonlinear optimization, dynamic programming, combinatorial optimization, Hopfield network
Language
English
Citation
Journal of Optimization Theory and Applications. New York: Springer/plenum Publishers, v. 128, n. 3, p. 563-580, 2006.
