Exponential synchronization of coupled neural networks under stochastic deception attacks

被引：43

作者：

Zhang, Huihui ^{[1
]}

Li, Lulu ^{[1
]}

Li, Xiaodi ^{[2
]}

机构：

[1] Hefei Univ Technol, Sch Math, Hefei 230009, Peoples R China

[2] Shandong Normal Univ, Sch Math & Stat, Jinan 250014, Peoples R China

来源：

NEURAL NETWORKS | 2022年 / 145卷

基金：

中国国家自然科学基金;

关键词：

Neural networks; Synchronization; Time delay; Stochastic impulses; Deception attacks; DYNAMICAL NETWORKS; DETECTION SCHEME; DELAYED SYSTEMS; NEUTRAL DELAYS; STABILITY;

D O I：

10.1016/j.neunet.2021.10.015

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the issue of synchronization is investigated for coupled neural networks subject to stochastic deception attacks. Firstly, a general differential inequality with delayed impulses is given. Then, the established differential inequality is further extended to the case of delayed stochastic impulses, in which both the impulsive instants and impulsive intensity are stochastic. Secondly, by modeling the stochastic discrete-time deception attacks as stochastic impulses, synchronization criteria of the coupled neural networks under the corresponding attacks are given. Finally, two numerical examples are provided to demonstrate the correctness of the theoretical results. (C) 2021 Elsevier Ltd. All rights reserved.

引用

页码：189 / 198

页数：10

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