Reliable anti-synchronization conditions for BAM memristive neural networks with different memductance functions

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• 作者：R. Sakthivel^a ; R. Anbuvithya^b ; K. Mathiyalagan ; ^c ; ^{kmathimath@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Yong-Ki Ma^d ; ^{ykma@kongju.ac.kr" class="auth_mail" title="E-mail the corresponding author} ; P. Prakash^b
• 关键词：Memristor ; Chaos ; Reliability ; BAM neural network ; Anti-synchronization
• 刊名：Applied Mathematics and Computation
• 出版年：2016
• 出版时间：15 February 2016
• 年：2016
• 卷：275
• 期：Complete
• 页码：213-228
• 全文大小：865 K

文摘

This paper is concerned with anti-synchronization results for a class of memristor-based bidirectional associate memory (BAM) neural networks with different memductance functions and time-varying delays. Based on drive-response system concept, differential inclusions theory and Lyapunov stability theory, some sufficient conditions are obtained to guarantee the reliable asymptotic anti-synchronization criterion for memristor-based BAM networks. The memristive BAM neural network is formulated for two types of memductance functions. Sufficient results are derived in terms of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed criterion is demonstrated through numerical example.