预防性反馈PCNN模型及在组合优化问题中的应用

马义德; 冯晓文; 绽琨; 赵荣昌; 李小军

doi:10.3969/j.issn.1001-0548.2013.05.018

预防性反馈PCNN模型及在组合优化问题中的应用

1.
兰州大学信息科学与工程学院兰州 730000;
2.
南特大学综合理工学院法国南特 44300

基金项目:

国家自然科学基金(61175012);教育部科技项目博士点基金(20110211110026);中央高校基金(lzujbky-2013-k06)

详细信息

作者简介:
马义德(1963-),男,博士,教授,主要从事数字图像处理、人工神经网络、嵌入式系统等方面的研究.

中图分类号: TP389.1
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出版历程
- 收稿日期: 2012-02-18
- 修回日期: 2013-06-19
- 刊出日期: 2013-10-14

Preventive Feedback PCNN Model and Its Application in the Combinatorial Optimization Problems

1.
School of Information Science&Engineering,Lanzhou University Lanzhou 730000;
2.
Ecole de Polytech,University of Nantes Nantes France 443000

摘要

摘要: 利用脉冲耦合神经网络(PCNN)的自动波特性求解组合优化问题。在三态层叠脉冲耦合神经网络(TCPCNN)模型基础上,结合三角不等式定理,构造具有预防性反馈的脉冲耦合神经网络模型。在搜索最优解的过程中,利用三角不等式定理对解进行预判断,不理想的解被删除,起到预防反馈作用,降低求解的空间复杂度,提高求解效率和准确性。将该算法应用于SP和TSP问题实验仿真,结果表明,该算法有效降低了解空间复杂度,进一步提高了搜索速度。
- 自动波 /
- 组合优化 /
- 预防性反馈 /
- 脉冲耦合神经网络
Abstract: An improved pulse coupled neural network (PCNN) model is proposed to solve combination optimization problem with help of PCNN auto-wave characteristic. Based on Tri-state cascading pulse coupled neural network (TCPCNN), a preventive feedback method by using the triangle inequality theorem is introduced. In the process of searching solutions, all solutions are judged by the triangle inequality theorem and solutions of poor quality are removed. Therefore, the solution space complexity of combinatorial optimization problems decreases and the efficiency and accuracy are improved. This algorithm is applied to the shor test path (SP) and the traveling salesman problem (TSP) simulations. The results show that the proposed algorithm can effectively reduce space complexity and further improve the searching speed.
- auto-wave /
- combinatorial optimization /
- preventive feedback /
- pulse coupled neural network