淡江大學機構典藏:Item 987654321/100422
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    题名: An Artificial Neural Network Approach to Multi-objective Programming and Multi-level Programming Problems
    作者: Shih, Hsu-Shih;Wen, Ue-Pyng;Lee, S.;Lan, Kuen-Ming;Hsiao, Han-Chyi
    贡献者: 淡江大學管理科學學系
    关键词: Neural network;Energy function;Multilevel programming;Multiobjective programming;Dynamic behavior
    日期: 2004-07
    上传时间: 2015-02-27 13:58:45 (UTC+8)
    出版者: Kidlington: Pergamon Press
    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
    显示于类别:[管理科學學系暨研究所] 期刊論文

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