Almost periodic dynamics of memristive inertial neural networks with mixed delays

Abstract Owing to the physical properties (switching behavior) of the memristor, the resistors in the VLSI circuit of inertial neural networks is exchanged by the memristors then the VLSI circuit is known as memristive inertial neural networks (MINNs). In this manuscript, the authors concentrate on examining the almost periodic dynamics of memristive inertial neural networks with mixed time delays. First, the considered MINNs model is converted into two first-order system with the support of an appropriate variable transformation. Then, by means of a matrix measure scheme and Halanay inequality, some sufficient criteria are achieved to guarantee the global exponential stability of the periodic solutions of MINNs with mixed time delays. Furthermore, our theoretical results on the almost periodicity of MINNs with mixed time delays is a newfangled. Finally, simulation examples are elucidated to spectacle the value of the attaining main results of this manuscript.

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