This article investigates the stabilization of discrete-time stochastic neural networks with time-varying delay via aperiodically intermittent control (AIC). A comprehensive analysis of the stabilization of discrete-time delayed systems via AIC is provided, where the Lyapunov function method and the Lyapunov–Krasovskii functional method are investigated, respectively. Then, three stabilization criteria are given, which extend previous works from the continuous-time framework to the discrete-time one, and the average activation time ratio (AATR) of AIC is estimated. It is highlighted that for the Lyapunov–Krasovskii functional method, a more flexible estimation for the AATR can be obtained. Finally, the differences and the advantages of the three stabilization criteria are illustrated by numerical simulations.

中文翻译：

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通过间歇控制稳定离散时间随机时滞神经网络

本文通过非周期性间歇控制 (AIC) 研究具有时变延迟的离散时间随机神经网络的稳定性。提供了通过 AIC 对离散时间延迟系统的稳定性进行的综合分析，其中分别研究了 Lyapunov 函数方法和 Lyapunov–Krasovskii 函数方法。然后，给出了三个稳定标准，将以前的工作从连续时间框架扩展到离散时间框架，并估计了 AIC 的平均激活时间比 (AATR)。需要强调的是，对于 Lyapunov–Krasovskii 函数方法，可以获得更灵活的 AATR 估计。最后，通过数值模拟说明了三种稳定准则的区别和优势。