New passivity criteria for memristor-based neutral-type stochastic BAM neural networks with mixed time-varying delays

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• 作者：M. Syed Ali^a ; ^{syedgru@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; R. Saravanakumar^a ; ^{saravanamaths30@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jinde Cao^b ; ^c ; ^{jdcao@seu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Passivity analysis ; Memristor ; Neutral-type stochastic BAM neural networks ; Quadruple integral ; Time-varying delay
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：1 January 2016
• 年：2016
• 卷：171
• 期：Complete
• 页码：1533-1547
• 全文大小：549 K

文摘

This paper deals with the problem of passivity analysis issue for a class of memristor-based neutral-type stochastic bidirectional associative memory neural networks (MNSBAMNNs) with discrete interval and distributed time-varying delays. By constructing new Lyapunov–Krasovskii functional (LKF) with quadruple integral terms and suitable activation function conditions, some delay-dependent passivity criteria are obtained in the linear matrix inequality (LMI) format. A numerical example is given to demonstrate the effectiveness and superiority of the new scheme.