Asymptotical mean square stability of cellular neural networks with random delay

ZHU En-wen^1,2, WANG Yong^3,4, ZHANG Han-jun², ZOU Jie-zhong⁵

1.School of Mathematics and Computational Science,Changsha Universify of Science and Technology,Changsha 410076,China;2.School of Mathematics and Computational Science,Xiangtan University,Xiangtan 411105,China;3.Dept. of Mathematics,Harbin Institute of Technology,Harbin 150001,China;4. School of Mathematics,Central South University,Changsha 410075,China;5.School of Mathematics,Central South University,Changsha 410075,China



Abstract:

In this paper,the asymptotical mean-square stability analysis problem is considered for a class of cellular neural networks (CNNs) with random delay. Compared with the previous work,the delay is modeled by a continuous-time homogeneous Markov process with a finite number of states. The main purpose of this paper is to establish easily verifiable conditions under which the random delayed cellular neural network is asymptotic mean-square stability. By using some stochastic analysis techniques and Lyapunov-Krasovskii functional,some conditions are derived to ensure that the cellular neural networks with random delay is asymptotical mean-square stability. A numerical example is exploited to show the vadlidness of the established results.

Key words:  cellular neural networks  asymptotical mean-square stability  random delay  linear matrix inequality

DOI£º10.11916/j.issn.1005-9113.2010.03.022

Clc Number:TP183

Fund: