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Passivity of memristor-based BAM neural networks with different memductance and uncertain delays
Anbuvithya, R.,Mathiyalagan, K.,Sakthivel, R.,Prakash, P. Springer Science + Business Media 2016 COGNITIVE NEURODYNAMICS - Vol.10 No.4
<P>This paper addresses the passivity problem for a class of memristor-based bidirectional associate memory (BAM) neural networks with uncertain time-varying delays. In particular, the proposed memristive BAM neural networks is formulated with two different types of memductance functions. By constructing proper Lyapunov-Krasovskii functional and using differential inclusions theory, a new set of sufficient condition is obtained in terms of linear matrix inequalities which guarantee the passivity criteria for the considered neural networks. Finally, two numerical examples are given to illustrate the effectiveness of the proposed theoretical results.</P>
Sakthivel, R.,Anbuvithya, R.,Mathiyalagan, K.,Ma, Y.K.,Prakash, P. Elsevier [etc.] 2016 Applied Mathematics and Computation Vol.275 No.-
<P>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. (C) 2015 Elsevier Inc. All rights reserved.</P>