Resilient fixed-time stabilization of switched neural networks subjected to impulsive deception attacks

被引：19

作者：

Bao, Yuangui ^{[1
,2
,3
]}

Zhang, Yijun ^{[1
]}

Zhang, Baoyong ^{[1
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China

[2] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China

[3] Yangtze Delta Reg Acad Beijing Inst Technol, Jiaxing 314000, Peoples R China

来源：

NEURAL NETWORKS | 2023年 / 163卷

基金：

中国国家自然科学基金;

关键词：

Fixed-time stabilization; Switched neural networks; Impulsive systems; Impulsive deception attacks; EXPONENTIAL STABILITY; VARYING DELAY; SYNCHRONIZATION; SYSTEMS; DESIGN;

D O I：

10.1016/j.neunet.2023.04.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article focuses on the resilient fixed-time stabilization of switched neural networks (SNNs) under impulsive deception attacks. A novel theorem for the fixed-time stability of impulsive systems is established by virtue of the comparison principle. Existing fixed-time stability theorems for impulsive systems assume that the impulsive strength is not greater than 1, while the proposed theorem removes this assumption. SNNs subjected to impulsive deception attacks are modeled as impulsive systems. Some sufficient criteria are derived to ensure the stabilization of SNNs in fixed time. The estimation of the upper bound for the settling time is also given. The influence of impulsive attacks on the convergence time is discussed. A numerical example and an application to Chua's circuit system are given to demonstrate the effectiveness of the theoretical results. (c) 2023 Elsevier Ltd. All rights reserved.

引用

页码：312 / 326

页数：15

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