A neural network approach to multiobjective and multilevel programming problems

淡江大學機構典藏 > 商管學院 > 管理科學學系暨研究所 > 期刊論文 > Item 987654321/27393

jsp.display-item.identifier=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

题名:	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
贡献者:	淡江大學經營決策學系
关键词:	Neural network;Energy function;Multilevel programming;Multiobjective programming;Dynamic behavior
日期:	2004-07
上传时间:	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
显示于类别:	[管理科學學系暨研究所] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
A neural network approach to multiobjective and multilevel programming problems.pdf		780Kb	Adobe PDF	30	检视/开启
index.html		0Kb	HTML	27	检视/开启

在機構典藏中所有的数据项都受到原著作权保护.

TAIR相关文章