中国物理B ›› 2009, Vol. 18 ›› Issue (8): 3325-3336.doi: 10.1088/1674-1056/18/8/037
佟绍成1, 张化光2, 浮洁3, 马铁东3
Zhang Hua-Guang(张化光)a)b)†, Fu Jie(浮洁)b), Ma Tie-Dong(马铁东)b), and Tong Shao-Cheng(佟绍成)c)
摘要: This paper is concerned with the problem of robust stability for a class of Markovian jumping stochastic neural networks (MJSNNs) subject to mode-dependent time-varying interval delay and state-multiplicative noise. Based on the Lyapunov--Krasovskii functional and a stochastic analysis approach, some new delay-dependent sufficient conditions are obtained in the linear matrix inequality (LMI) format such that delayed MJSNNs are globally asymptotically stable in the mean-square sense for all admissible uncertainties. An important feature of the results is that the stability criteria are dependent on not only the lower bound and upper bound of delay for all modes but also the covariance matrix consisting of the correlation coefficient. Numerical examples are given to illustrate the effectiveness.
中图分类号: (Stochastic analysis)