Neural Approach for Solving Several Types of Optimization Problems

I. N. da Silva, W. C. Amaral and L. V. R. Arruda
Additional contact information
I. N. da Silva: State University of São Paulo
W. C. Amaral: University of Campinas
L. V. R. Arruda: CEFET-PR/CPGEI

Journal of Optimization Theory and Applications, 2006, vol. 128, issue 3, No 5, 563-580

Abstract: 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.

Keywords: Recurrent neural networks; nonlinear optimization; dynamic programming; combinatorial optimization; Hopfield network. (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10957-006-9032-9

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