AN EFFECTIVE ALGORITHM FOR THE MINIMUM SET COVER PROBLEM

摘要：

In this paper, a learning algorithm of the Hopfield neural network, which can escape from local minimum, is proposed.The learning algorithm adjusts the balance between constraint term and cost term of the energy function so that the local minimum that the network once falls into vanishes and the network can continue updating in a gradient descent direction of energy. Approximation performance is experimentally determined on random instances of hypergraphs by comparing it to several known algorithms. The experimental results show that the proposed algorithm works much better than the existing algorithms for the problem.

关键词： Hopfield Neural Network Local Minimum Learning Set Cover Problem

作者: PEI ZHANG RONG-LONG WANG CHONG-GUANG WU KOZO OKAZAKI

作者单位: Faculty of Information Science and Technology, Beijing University of Chemical Technology, Beijing, C Faculty of Engineering, Fukui University, Fukui-shi, Japan 910-8507

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3032-3035

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）