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Analysis Of The Stability Of A Class Of Uncertain Stochastic Differential Systems

Posted on:2011-11-03Degree:MasterType:Thesis
Country:ChinaCandidate:M Z LuoFull Text:PDF
GTID:2208360308466952Subject:Applied Mathematics
Abstract/Summary:PDF Full Text Request
Networks play on a very important role in our lives. In the fact, most of the things that control our lives, like power transformation, transportation, cooperation, gene regulatory and artificial networks. In order to better understand the dynamical of different kinds of networks, an important problem is to study the behavior of stability of networks. This paper investigates the robust stability criteria for stochastic uncertain networks with time-varying delays and our conclusions can be described as following:At first, we deal with the problem of globally robust asymptotical stability in the mean square for a class of uncertain continuous-time stochastic neural networks with mixed delays. By employing the inequality methods and establishing some suitable Laypunov-functional we attain some much better stability criterias about addressed systems, finally one numerical example has been given to demonstrate the effectiveness of our results.Secondly, continuous-time manner neural networks have been attained a lot of important conclusions. However, in implementing and applications of neural networks, we discover that discrete-time neural networks are much more important than their continuous-time counterpart. Therefore, in this paper we study the stability problem of discrete-time uncertain stochastic neural networks. By using some well-know inequalities, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the neural networks to be globally, robustly, exponential stable.Finally, we introduce the term of the distributed time delays in the discrete-time systems. By employing the Lyapunov functional combined the generalized free weighing matrix method and feedback control technique, we obtain several delay-dependent conditions that ensure the systems to be globally robust asymptotical stability in the mean square.
Keywords/Search Tags:Stochastic neural networks, Linear matrix inequality, Delay-dependent criteria, Robust asymptotical stability, Exponential stability in the mean square
PDF Full Text Request
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