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.
出处
《重庆大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
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)
关键词
最大团问题
启发式算法
神经网络
非线性自反馈
maximum clique problem
heuristic algorithm
neural network
non-linear self-feedback