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    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/27393

    題名: A neural network approach to multiobjective and multilevel programming problems
    作者: 時序時;Shih, Hsu-shih;Wen, Ue-pyng;Lee, S.;Lan, Kuen-ming;Hsiao, Han-chyi
    貢獻者: 淡江大學經營決策學系
    關鍵詞: Neural network;Energy function;Multilevel programming;Multiobjective programming;Dynamic behavior
    日期: 2004-07
    上傳時間: 2009-12-30 15:08:17 (UTC+8)
    出版者: Elsevier
    摘要: This study aims at utilizing the dynamic behavior of artificial neural networks (ANNs) to solve multiobjective programming (MOP) and multilevel programming (MLP) problems. The traditional and non-traditional approaches to the MLP are first classified into five categories. Then, based on the approach proposed by Hopfield and Tank [1], the optimization problem is converted into a system of nonlinear differential equations through the use of an energy function and Lagrange multipliers. Finally, the procedure is extended to MOP and MLP problems. To solve the resulting differential equations, a steepest descent search technique is used. This proposed nontraditional algorithm is efficient for solving complex problems, and is especially useful for implementation on a large-scale VLSI, in which the MOP and MLP problems can be solved on a real time basis. To illustrate the approach, several numerical examples are solved and compared.
    關聯: Computers & Mathematics with Applications 48(1-2), pp.95-108
    DOI: 10.1016/j.camwa.2003.12.003
    顯示於類別:[管理科學學系暨研究所] 期刊論文


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