Stability criteria for Markovian jump neural networks with mode-dependent additive time-varying delays via quadratic convex combination

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• 作者：P. Muthukumarp> ; p>p>pmuthukumargri@gmail.com" class="auth_mail" title="E-mail the corresponding authorp>Author Vitae ; K. SubramanianAuthor Vitae
• 关键词：Additive time-varying delays ; Markovian jump parameters ; Mode-dependent time-varying delays ; Neural networks ; Quadratic convex combination ; Stability
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：12 September 2016
• 年：2016
• 卷：205
• 期：Complete
• 页码：75-83
• 全文大小：615 K

文摘

This paper is mainly concerned on stability problem of Markovian jump neural networks with mode-dependent two additive time-varying delays based on quadratic convex combination approach. The jumping parameters are modeled as a continuous time, finite state Markov chain. By constructing a suitable augmented Lyapunov–Krasovskii functional, utilizing the Jensen׳s inequality, the idea of second order convex combination and the property of quadratic convex function, the sufficient conditions are derived to guarantee that the proposed neural networks are globally asymptotically stable. Moreover, these stability criteria are expressed in terms of linear matrix inequalities, which can be efficiently solved via the standard numerical packages. Finally, the numerical examples are given to validate the less conservatism and effectiveness of the theoretical results.