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.

由于忆阻器的物理特性（开关行为），惯性神经网络的VLSI电路中的电阻器被忆阻器交换，因此VLSI电路被称为忆阻惯性神经网络（MINN）。在本手稿中，作者集中于研究具有混合时滞的忆阻惯性神经网络的几乎周期性动力学。首先，在适当的变量转换的支持下，将考虑的MINN模型转换为两个一阶系统。然后，通过矩阵测度方案和Halanay不等式，获得了一些足够的准则，以保证具有混合时滞的MINNs周期解的全局指数稳定性。此外，我们关于具有混合时滞的MINN的几乎周期性的理论结果是新的。最后，