Exponential mean-square filtering for arbitrarily switched neural networks with missing measurements

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• 作者：Yan Che^a ; ^bAuthor Vitae ; Huisheng Shu^a ; ^{hsshu@dhu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; ^{hsshudh@tom.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yurong Liu^c ; ^dAuthor Vitae
• 关键词：Missing measurements ; Discrete-time neural networks ; Switched systems ; H&infin ; H&infin ; filtering ; Arbitrary switched neural networks (ASNNs)
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：12 June 2016
• 年：2016
• 卷：193
• 期：Complete
• 页码：227-234
• 全文大小：572 K

文摘

In this paper, the H_∞$H_{\infty }$ filtering problem is investigated for a class of discrete-time arbitrary switched neural networks with missing measurements, stochastic perturbations, and communication delays. Based on the average dwell time approach and a set of Kronecker delta functions, a unified measurement model is established to represent the phenomena of missing measurements, time delays and nonlinearities. The aim of this paper is to design an H_∞$H_{\infty }$ filter such that the filter error dynamics is exponentially mean-square stable and the H_∞$H_{\infty }$ performance requirement is satisfied simultaneously. By using the Lyapunov stability theory and the matrix technology, the design method of the desired filter is given in terms of a matrix inequality which can be solved by using the available software. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.