Passivity analysis for uncertain stochastic neural networks with discrete interval and distributed time-varying delays  

作　　者：P.Balasubramaniam G.Nagamani 

机构地区：[1]Department of Mathematics,Gandhigram Rural University

出　　处：《Journal of Systems Engineering and Electronics》2010年第4期688-697,共10页系统工程与电子技术（英文版）

基　　金：supported by Department of Science and Technology,New Delhi,India(SR/S4/MS:485/07)

摘　　要：The problem of passivity analysis is investigated for uncertain stochastic neural networks with discrete interval and distributed time-varying delays.The parameter uncertainties are assumed to be norm bounded and the delay is assumed to be time-varying and belongs to a given interval,which means that the lower and upper bounds of interval time-varying delays are available.By constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique,new delay-dependent passivity conditions are derived in terms of linear matrix inequalities（LMIs）.Finally,numerical examples are given to show the less conservatism of the proposed conditions.The problem of passivity analysis is investigated for uncertain stochastic neural networks with discrete interval and distributed time-varying delays.The parameter uncertainties are assumed to be norm bounded and the delay is assumed to be time-varying and belongs to a given interval,which means that the lower and upper bounds of interval time-varying delays are available.By constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique,new delay-dependent passivity conditions are derived in terms of linear matrix inequalities（LMIs）.Finally,numerical examples are given to show the less conservatism of the proposed conditions.

关 键 词：linear matrix inequality（LMI） stochastic neural network PASSIVITY interval time-varying delay Lyapunov method. 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]