用带非线性自反馈的神经网络求解最大团问题  

Using a Neural Network with Nonlinear Self-feedback to Solve the Maximum Clique Problem

作　　者：刘怀义[1] 杨小帆[1] 孙丽萍[1] 司沛[1] 王灿[1] 

机构地区：[1]重庆大学计算机学院,重庆400030

出　　处：《重庆大学学报（自然科学版）》2007年第9期60-63,共4页Journal of Chongqing University

基　　金：教育部新世纪优秀人才资助计划(NCET-05-0759);教育部博士点基金资助项目(20050611001);重庆市自然科学基金资助项目(CSTC2006BB2231CSTC2005BB2191)

摘　　要：针对饱和非线性动态网络算法(SLDN算法)解最大团问题容易陷入局部最优这一缺点,提出了解决该问题的一种新的神经网络算法,并构建了新数学模型。该算法在SLDN算法基础上加入了非线性自反馈,具备良好的动力学特性。分析了加入非线性自反馈后的收敛性,并且通过仿真实验表明其整体性能要优于SLDN算法。As the MCP is NP-hard, an efficient approach to treating this problem is to design appropriate recurrent neural networks. We develop a new algorithm for the MCP, which can, to a certain extent, prevent the associated neural network from falling into local optimal points. The proposed algorithm incorporates nonlinear self-feedback into the SLDN algorithm and has distinguished dynamical characteristics. Simulation results show that the performance of proposed algorithm is statistically superior to the SLDN algorithm.

关 键 词：最大团问题 启发式算法 神经网络 非线性自反馈 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]