期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 53, 期 3, 页码 2017-2027出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2021.3108574
关键词
Stability criteria; Delays; Numerical stability; Lyapunov methods; Delay effects; Biological neural networks; Synchronization; Discrete-time systems; intermittent control; neural networks; stabilization; stochastic systems; time delay
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.
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.
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