Stabilization of Discrete-Time Stochastic Delayed Neural Networks by Intermittent Control

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.

Automated summary

This article investigates the stabilization of discrete-time stochastic neural networks with time-varying delay using aperiodically intermittent control (AIC). It provides a comprehensive analysis of the stabilization of discrete-time delayed systems through AIC, exploring the Lyapunov function method and the Lyapunov-Krasovskii functional method. Three stabilization criteria are then presented, extending previous works from continuous-time to discrete-time frameworks, and the average activation time ratio (AATR) of AIC is estimated. It is highlighted that a more flexible estimation for the AATR can be obtained using the Lyapunov-Krasovskii functional method. Finally, numerical simulations are used to illustrate the differences and advantages of the three stabilization criteria.