New results on periodic dynamics of memristor-based recurrent neural networks with time-varying delays

Abstract In this brief, we study a class of memristor-based recurrent neural networks (MRNNs) with time-varying delays. Easily verifiable delay-independent criteria are established to ensure the existence and global exponential stability of periodic solutions by using novel analysis techniques, which not only improve but also complement some existing ones. These theoretical results are also supported with numerical simulations.

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