文献类型：期刊文献

英文题名：EXPONENTIAL STABILITY OF PERIODIC SOLUTIONS FOR INERTIAL COHEN-GROSSBERG-TYPE NEURAL NETWORKS

作者：Ke, Yunquan[1];Miao, Chunfang[1]

机构：[1]Shaoxing Univ, Dept Math, Shaoxing 312000, Peoples R China

年份：2014

卷号：24

期号：4

起止页码：377

外文期刊名：NEURAL NETWORK WORLD

收录：SCI-EXPANDED(收录号：WOS:000341614500004)、、EI(收录号：20153901308967)、Scopus(收录号：2-s2.0-84942100891)、WOS

基金：This work is supported by the Natural Science Foundation of Zhejiang Province (No.Y6100096).

语种：英文

外文关键词：Inertial Cohen-Grossberg-type neural networks; Lyapunov function; inequality technique; periodic solutions; exponential stability

外文摘要：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.