Non-fragile finite-time state estimation for discrete-time Markov jump neural networks with unreliable communication links

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• 作者：Feng Li ; Hao Shen ; ^{haoshen10@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; ^{haoshenwj10@163.com" class="auth_mail" title="E-mail the corresponding author} ; Mengshen Chen ; Qingkai Kong
• 关键词：Markov jump neural networks ; Finite-time l2&minus ; l&infin ; l2&minus ; l&infin ; state estimation ; Non-fragile estimator ; Unreliable communication links
• 刊名：Applied Mathematics and Computation
• 出版年：2015
• 出版时间：15 November 2015
• 年：2015
• 卷：271
• 期：Complete
• 页码：467-481
• 全文大小：481 K

文摘

This paper is concerned with the problem of finite-time l₂−l_∞$l_2-l_{\infty }$ non-fragile state estimation for discrete-time Markov jump neural networks with unreliable communication links. The simultaneous occurrences of packet dropouts, time delays and the sensor nonlinearity stemmed from the unreliable communication links are considered. The focus is on the design of non-fragile state estimator such that the augmented estimation error system is mean-square stochastically finite-time stable with a prescribed level of l₂−l_∞$l_2-l_{\infty }$ performance. By employing Lyapunov–Krasovskii approach and finite-time analysis theory, some sufficient conditions have been obtained for the existence of an admissible state estimator. Finally, a numerical example is employed to demonstrate the effectiveness of our proposed approach.