Delay-probability-distribution-dependent robust stability analysis for stochastic neural networks with time-varying delay  

作　　者：Jie Fu,Huaguang Zhang,Tiedong Ma School of Information Science and Engineering,Northeastern University,Box 134,Shenyang 110004,China 

出　　处：《Progress in Natural Science:Materials International》2009年第10期1333-1340,共8页自然科学进展·国际材料（英文版）

基　　金：supported by the National Natural Science Foundation of China(60534010,60572070,60774048,60804006 and 60728307);the Program for Cheung Kong Scholars and Innovative Research Groups ofChina (60521003);the Research Fund for the Doctoral Program of China Higher Education (20070145015);the National High Technology Research and Development Program of China (2006AA04Z183)

摘　　要：The delay-probability-distribution-dependent robust stability problem for a class of uncertain stochastic neural networks(SNNs) with time-varying delay is investigated.The information of probability distribution of the time delay is considered and transformed into parameter matrices of the transferred SNNs model.Based on the Lyapunov-Krasovskii functional and stochastic analysis approach,a delay-probability-distribution-dependent sufficient condition is obtained in the linear matrix inequality(LMI) format such that delayed SNNs are robustly globally asymptotically stable in the mean-square sense for all admissible uncertainties.An important feature of the results is that the stability conditions are dependent on the probability distribution of the delay and upper bound of the delay derivative,and the upper bound is allowed to be greater than or equal to 1.Finally,numerical examples are given to illustrate the effectiveness and less conservativeness of the proposed method.The delay-probability-distribution-dependent robust stability problem for a class of uncertain stochastic neural networks （SNNs） with time-varying delay is investigated. The information of probability distribution of the time delay is considered and transformed into parameter matrices of the transferred SNNs model. Based on the Lyapunov-Krasovskii functional and stochastic analysis approach, a delay-probability-distribution-dependent sufficient condition is obtained in the linear matrix inequality （LMI） format such that delayed SNNs are robustly globally asymptotically stable in the mean-square sense for all admissible uncertainties. An important feature of the results is that the stability conditions are dependent on the probability distribution of the delay and upper bound of the delay derivative, and the upper bound is allowed to be greater than or equal to 1.Finally, numerical examples are given to illustrate the effectiveness and less conservativeness of the proposed method.

关 键 词：Delay-probability-distribution-dependent Stochastic neural networks Time-varying delay Linear matrix inequality 

分 类 号：O231.3[理学—运筹学与控制论]