This paper addresses the asymptotic stabilization of memristive neural networks with mixed time-varying delays. With two different sampling schemes, sufficient conditions for asymptotic stability of the delayed memristive neural networks system can be obtained by designing appropriate event-based controllers. It is worth mentioning that the state-dependent memristive neural network model in this paper includes time-varying discrete and distributed delays, which is a generalization of the traditional neural network model. Furthermore, based on the continuous sampling event trigger control scheme, a method for designing more economical periodic sampling event trigger control scheme is proposed. Finally, to verify the validity of our conclusions, two numerical simulation examples are given.

本文讨论具有混合时变时滞的忆阻神经网络的渐近稳定。使用两种不同的采样方案，可以通过设计适当的基于事件的控制器来获得延迟忆阻神经网络系统的渐近稳定性的充分条件。值得一提的是，基于状态的忆阻神经网络模型包括时变的离散和分布式时延，是对传统神经网络模型的概括。此外，基于连续采样事件触发控制方案，提出了一种设计更经济的周期性采样事件触发控制方案的方法。最后，为验证我们的结论的有效性，给出了两个数值模拟示例。