Stability of stochastic neural networks with Markovian jumping parameters

被引：0
|
作者：
Hua Mingang
机构：
来源：
JournalofSystemsEngineeringandElectronics | 2009年 / 20卷 / 03期
基金：
中国国家自然科学基金;
关键词：
stochastic neural networks; global asymptotical stability; linear matrix inequality; Markovian jumping parameters;
D O I：
暂无
中图分类号：
TP183 [人工神经网络与计算];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The global asymptotical stability for a class of stochastic delayed neural networks(SDNNs)with Markovian jumping parameters is considered.By applying Lyapunov functional method and It(?)’s differential rule, new delay-dependent stability conditions are derived.All results are expressed in terms of linear matrix inequality (LMI),and a numerical example is presented to illustrate the correctness and less conservativeness of the proposed method.
引用
收藏
页码：613 / 618
页数：6
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