Exponential stability criterion for Cohen-Grossberg neural networks with time-varying delay

被引:0
|
作者
Li Tao [1 ]
Fei Shumin [1 ]
机构
[1] Southeast Univ, Res Inst Automat, Nanjing 210096, Jiangsu, Peoples R China
关键词
exponential stability; descriptor system; Cohen-Grossberg neural networks; time-delay; LMI;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, the global exponential stability is investigated for the Cohere-Grossberg neural networks with time-varying delay. By using the appropriate Lyapunov-Krasovskii functional and equivalent descriptor form of the considered system, an LMI-based delay-dependent sufficient condition is obtained to guarantee the exponential stability of the addressed neural networks, which can be checked readily by resorting to the Matlab LMI toolbox. A numerical example is given to show the effectiveness and less conservatism of the obtained methods.
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页码:171 / +
页数:2
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