Non-fragile synchronization of memristor-based neural networks using passivity theory

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• 作者：K. Mathiyalagan^a ; R. Anbuvithya^b ; R. Sakthivel^c ; ^{krsakthivel@yahoo.com" class="auth_mail" title="E-mail the corresponding author} ; Ju H. Park^a ; ^{jessie@ynu.ac.kr" class="auth_mail" title="E-mail the corresponding author} ; P. Prakash^b
• 关键词：Memristor ; Recurrent neural networks ; Random uncertainties ; Non-fragile control ; Uncertain delay
• 刊名：Neural Networks
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：74
• 期：Complete
• 页码：85-100
• 全文大小：592 K

文摘

In this paper, we formulate and investigate the mixed H_∞$H_{\infty }$ and passivity based synchronization criteria for memristor-based recurrent neural networks with time-varying delays. Some sufficient conditions are obtained to guarantee the synchronization of the considered neural network based on the master–slave concept, differential inclusions theory and Lyapunov–Krasovskii stability theory. Also, the memristive neural network is considered with two different types of memductance functions and two types of gain variations. The results for non-fragile observer-based synchronization are derived in terms of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed criterion is demonstrated through numerical examples.