The Almost Sure Exponential Stability of Discrete-time Impulsive Stochastic Cohen-Grossberg Neural Network Systems

Xiao Gao; Dianqiang Li; Yan Lu; Qiaoyu Ma

doi:10.6919/ICJE.202503_11(3).0018

Authors

Xiao Gao
Dianqiang Li
Yan Lu
Qiaoyu Ma

DOI:

https://doi.org/10.6919/ICJE.202503_11(3).0018

Keywords:

Cohen-Grossberg Neural Network; Discrete-time; Impulsive System; Lyapunov Function.

Abstract

This paper investigates the almost sure exponential stability of discrete-time impulsive stochastic Cohen-Grossberg neural network systems (CGNNs). Firstly, for the linear scalar system, sufficient conditions for the almost sure exponential stability of the neural network system are presented. Secondly, by using the average impulsive interval method and the strong law of large numbers, sufficient conditions for the almost sure exponential stability of the general discrete-time impulsive neural network system are given. Finally, the validity of the conclusions is verified through the numerical simulations.

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