Improved transiently chaotic neural network and its application to optimization

被引：0
|
作者：
Xu, Yao-qun [1 ]
Sun, Ming
Guo, Meng-Shu
机构：
[1] Harbin Univ Commerce, Inst Syst Engn, Harbin 150028, Peoples R China
[2] Harbin Inst Technol, Ctr Control Theory & Guidance Technol, Harbin 150001, Peoples R China
来源：
NEURAL INFORMATION PROCESSING, PT 2, PROCEEDINGS | 2006年 / 4233卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A wavelet function was introduced into the activation function of the transiently chaotic neural network in order to solve combinational optimization problems more efficiently. The dynamic behaviors of chaotic signal neural units were analyzed and the time evolution figures of the maximal Lyapunov exponents and chaotic dynamic behavior were given. The improved transiently chaotic neural network has the ability to stay in chaotic states longer because the wavelet function is non-monotonous and is a.,kind of basic function. The simulation results prove that the improved transiently chaotic neural network is superior to the original in solving 10-city traveling salesman problem (TSP).
引用
收藏
页码：1032 / 1041
页数：10
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