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Neural Approach for Solving Several Types of Optimization Problems

Author

Listed:
  • I. N. da Silva

    (State University of São Paulo)

  • W. C. Amaral

    (University of Campinas)

  • L. V. R. Arruda

    (CEFET-PR/CPGEI)

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.

Suggested Citation

  • I. N. da Silva & W. C. Amaral & L. V. R. Arruda, 2006. "Neural Approach for Solving Several Types of Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 128(3), pages 563-580, March.
    • Handle: RePEc:spr:joptap:v:128:y:2006:i:3:d:10.1007_s10957-006-9032-9
    DOI: 10.1007/s10957-006-9032-9
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    Cited by:

    1. Wen, Ue-Pyng & Lan, Kuen-Ming & Shih, Hsu-Shih, 2009. "A review of Hopfield neural networks for solving mathematical programming problems," European Journal of Operational Research, Elsevier, vol. 198(3), pages 675-687, November.

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