Generalized LMI-based approach to global asymptotic stability of cellular neural networks with delay  

作　　者：刘德友 张建华 关新平 肖晓丹 

机构地区：[1]College of Science,Yanshan University,Qinhuangdao 066004,Hebei Province,P.R.China

出　　处：《Applied Mathematics and Mechanics(English Edition)》2008年第6期811-816,共6页应用数学和力学（英文版）

基　　金：Project supported by the National Natural Science Foundation of China (No.60604004);the Natural Science Foundation of Hebei Province of China (No.F2007000637);the National Natural Science Foundation for Distinguished Young Scholars (No.60525303)

摘　　要：A global asymptotic stability problem of cellular neural networks with delay is investigated. A new stability condition is presented based on the Lyapunov-Krasovskii method, which is dependent on the amount of delay. A result is given in the form of a linear matrix inequality, and the admitted upper bound of the delay can be easily obtained. The time delay dependent and independent results can be obtained, which include some previously published results. A numerical example is given to show the effectiveness of the main results.A global asymptotic stability problem of cellular neural networks with delay is investigated. A new stability condition is presented based on the Lyapunov-Krasovskii method, which is dependent on the amount of delay. A result is given in the form of a linear matrix inequality, and the admitted upper bound of the delay can be easily obtained. The time delay dependent and independent results can be obtained, which include some previously published results. A numerical example is given to show the effectiveness of the main results.

关 键 词：delayed cellular neural networks （DCNNs） linear matrix inequality （LMI） global stability 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]