A Neural Network for Solving Nonlinear Multilevel Programming Problems

摘要：

This paper aims at utilizing the dynamic behavior of artificial neural networks to solve nonlinear multilevel programming (MLP) problems. Across complementarily slackness conditions base on entropic regularization, the optimization problem is converted into a system of nonlinear differential equations through use of an energy function and Lagrange multipliers. To solve the resulting differential equations, a steepest descent search technique is used. This proposed nontraditional algorithm is efficient for solving complex problems, and MLP problems can be solved on a real time basis. To illustrate the approach, several numerical examples are solved and compared.

关键词： multilevel programming problems artificial neural networks entropic regularization optimal solution

作者: Feng Xiangdong Hu Guanghua

作者单位: School of Mathematics and Statistics of Yunnan University, Yunnan Kunming, P.R. China, 650091 The En School of Mathematics and Statistics of Yunnan University, Yunnan Kunming, P.R. China, 650091

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 1521-1526

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）