EXPONENTIAL STABILITY OF PERIODIC SOLUTIONS FOR INERTIAL COHEN-GROSSBERG-TYPE NEURAL NETWORKS

被引:12
作者
Ke, Yunquan [1 ]
Miao, Chunfang [1 ]
机构
[1] Shaoxing Univ, Dept Math, Shaoxing 312000, Peoples R China
关键词
Inertial Cohen-Grossberg-type neural networks; Lyapunov function; inequality technique; periodic solutions; exponential stability; EXISTENCE;
D O I
10.14311/NNW.2014.24.022
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, the exponential stability of periodic solutions for inertial Cohen-Grossberg-type neural networks are investigated. First, by properly chosen variable substitution the system is transformed to first order differential equation. Second, some sufficient conditions which can ensure the existence and exponential stability of periodic solutions for the system are obtained by using constructing suitable Lyapunov function and differential mean value theorem, applying the analysis method and inequality technique. Finally, two examples are given to illustrate the effectiveness of the results.
引用
收藏
页码:377 / 394
页数:18
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