state estimation for memristive neural networks with time-varying delays: The discrete-time case

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• 作者：Sanbo Ding ^{dingsanbo@163.com" class="auth_mail" title="E-mail the corresponding author} ; Zhanshan Wang ; ^{zhanshan_wang@163.com" class="auth_mail" title="E-mail the corresponding author} ; Jidong Wang ^{kfwjd@ncwu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Huaguang Zhang ^{hgzhang@ieee.org" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Discrete-time memristive neural networks ; H&infin ; H&infin ; state estimation ; Robust analysis method ; Time-varying delays
• 刊名：Neural Networks
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：84
• 期：Complete
• 页码：47-56
• 全文大小：587 K

文摘

This paper investigates the H_∞${\mathcal{H}}_{\infty }$ state estimation problem for a class of discrete-time memristive neural networks (DMNNs) with time-varying delays. For the sake of coping with the switched weight matrices, the DMNNs are recast into a tractable model by defining a series of state-dependent switched signals. Based on the tractable model, the robust analysis method and Lyapunov stability theory are developed to devise a sufficient condition which ensures the global asymptotical stability of the estimation error system with a prescribed H_∞${\mathcal{H}}_{\infty }$ performance. The desired state estimator gain matrix and optimal performance index can be accomplished via solving a convex optimization problem subject to several linear matrix inequalities (LMIs). Finally, one numerical example is presented to check the effectiveness of the theoretical results.