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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/27396

    Title: A review of Hopfield neural networks for solving mathematical programming problems
    Authors: Wena, Ue-Pyng;Lan, Kuen-Ming;Shih, Hsu-Shih
    Contributors: 淡江大學經營決策學系
    Keywords: Hopfield neural networks;Energy function;Mathematical programming;penalty function;Lagrange multiplier;Primal and dual functions
    Date: 2009-11-01
    Issue Date: 2009-12-30 15:09:26 (UTC+8)
    Publisher: Amsterdam: Elsevier BV * North-Holland
    Abstract: The Hopfield neural network (HNN) is one major neural network (NN) for solving optimization or mathematical programming (MP) problems. The major advantage of HNN is in its structure can be realized on an electronic circuit, possibly on a VLSI (very large-scale integration) circuit, for an on-line solver with a parallel-distributed process. The structure of HNN utilizes three common methods, penalty functions, Lagrange multipliers, and primal and dual methods to construct an energy function. When the function reaches a steady state, an approximate solution of the problem is obtained. Under the classes of these methods, we further organize HNNs by three types of MP problems: linear, non-linear, and mixed-integer. The essentials of each method are also discussed in details. Some remarks for utilizing HNN and difficulties are then addressed for the benefit of successive investigations. Finally, conclusions are drawn and directions for future study are provided.
    Relation: European Journal of Operational Research 198(3), pp.675-687
    DOI: 10.1016/j.ejor.2008.11.002
    Appears in Collections:[Department of Management Sciences] Journal Article

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