state estimation for memristive neural networks with multiple fading measurements

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• 作者：Le Yan^aAuthor Vitae ; Sunjie Zhang^a ; ^{zhang_sunjie@126.com}Author Vitae ; Derui Ding^aAuthor Vitae ; Yurong Liu^b ; ^cAuthor Vitae ; Fuad E. Alsaadi^cAuthor Vitae
• 关键词：Memristive neural networks ; H&infin ; state estimation ; Sector-bounded nonlinearities ; Multiple fading measurements
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：22 March 2017
• 年：2017
• 卷：230
• 期：Complete
• 页码：23-29
• 全文大小：532 K
• 卷排序：230

文摘

The attention of this paper is mainly concentrated on the H∞H∞ state estimator's design problem for a kind of discrete-time memristive neural networks (MNNs) with multiple fading measurements. The phenomenon of multiple fading measurements is represented by a set of individual stochastic variables obeying a predetermined distribution on interval [0,1]. Firstly, the augmented system comprised of MNNs and the dynamics of estimation errors are put forward to implement the performance analysis. Then, under the framework of the difference inclusion theory combined with the Lyapunov function method, several sufficient conditions are established to guarantee the exponential mean-square stability as well as H∞H∞ performance index. Furthermore, the desired estimator parameter is obtained in view of the solution of a convex optimization problem. In the end, an illustrative numerical example is exploited to check the reliability and usefulness of the design scheme in this paper.