Stability analysis of Markovian jumping stochastic Cohen-Grossberg neural networks with mixed time delays

被引：268

作者：

Zhang, Huaguang ^{[1
,2
]}

Wang, Yingchun ^{[3
]}

机构：

[1] Northeastern Univ, Dept Automat Control, Shenyang 110004, Peoples R China

[2] Northeastern Univ, Natl Educ Minist, Key Lab Integrated Automat Proc Ind, Shenyang 110004, Peoples R China

[3] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Liaoning, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2008年 / 19卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Cohen-Grossberg neural networks (CGNNs); delay-dependent criteria; linear matrix inequality (LMI); Markovian jumping; mixed delay;

D O I：

10.1109/TNN.2007.910738

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 letter, the global asymptotical stability analysis problem is considered for a class of Markovian jumping stochastic Cohen-Grossberg neural networks (CGNNs) with mixed delays including discrete delays and distributed delays. An alternative delay-dependent stability analysis result is established based on the linear matrix inequality (LMI) technique, which can easily be checked by utilizing the numerically efficient Matlab LMI toolbox. Neither system transformation nor free-weight matrix via Newton-Leibniz formula is required. Two numerical examples are included to show the effectiveness of the result.

引用

页码：366 / 370

页数：5

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