Mean square stability of recurrent neural networks with random delay and Markovian switching

被引：0

作者：

朱恩文 ^{[1
,2
]}

王勇 ^{[3
]}

张汉君 ^{[2
]}

邹捷中 ^{[4
]}

机构：

[1] School of Mathematics and Computational Science,Changsha University of Science and Technology

[2] School of Mathematics and Computational Science,Xiangtan University

[3] Dept.of Mathematics,Harbin Institute of Technology

[4] School of Mathematics,Central South University

来源：

Journal of Harbin Institute of Technology(New series) | 2009年 / 16卷 / 05期

基金：

中国国家自然科学基金;

关键词：

recurrent neural networks; mean-square stability; random delay; Markovian switching; linear matrix inequality;

D O I：

暂无

中图分类号：

O781 [晶体生长理论]; O734 [晶体的光学性质];

学科分类号：

0702 ; 070205 ; 0703 ; 0803 ; 080501 ;

摘要：

To establish easily proved conditions under which the random delayed recurrent neural network with Markovian switching is mean-square stability,the evolution of the delay was modeled by a continuous-time homogeneous Markov process with a finite number of states.By employing Lyapunov-Krasovskii functionals and conducting stochastic analysis,a linear matrix inequality (LMI) approach was developed to derive the criteria for mean-square stability,which can be readily checked by some standard numerical packages such as the Matlab LMI Toolbox.A numerical example was exploited to show the usefulness of the derived LMI-based stability conditions.

引用

页码：678 / 682

页数：5

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