Mean square exponential stability for discrete-time stochastic fuzzy neural networks with mixed time-varying delay

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• 作者：Di Liu^a ; ^{bhuliudi2013@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Lijie Wang^a ; ^{lijiewang1@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yingnan Pan^a ; ^{panyingnan0803@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Haoyi Ma^b ; ^{1030845405@qq.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Interval type-2 (IT2) fuzzy systems ; Neural networks ; Discrete-time stochastic systems ; Passivity analysis ; Mean square exponential stability
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：1 January 2016
• 年：2016
• 卷：171
• 期：Complete
• 页码：1622-1628
• 全文大小：457 K

文摘

This paper concerns with the passivity and mean square exponential stability problems for discrete-time stochastic fuzzy neural network with parameter uncertainties based on an interval type-2 (IT2) fuzzy model. For the nonlinear stochastic fuzzy neural network, novel sufficient conditions are presented by linear matrix inequalities (LMIs) to guarantee the passivity and mean square exponential stability of the resulting system, and the parameter uncertainties are handled via the IT2 fuzzy model approach. The main contribution of this paper is that we first propose the IT2 T–S discrete-time stochastic fuzzy neural network. Finally, a numerical example is provided to testify the effectiveness of the proposed scheme.