Synchronization of coupled switched neural networks subject to hybrid stochastic disturbances

被引：29

作者：

Long, Han ^{[1
]}

Ci, Jingxuan ^{[2
]}

Guo, Zhenyuan ^{[2
]}

Wen, Shiping ^{[3
]}

Huang, Tingwen ^{[4
]}

机构：

[1] Natl Univ Def Technol, Coll Sci, Changsha 410073, Peoples R China

[2] Hunan Univ, Sch Math, Changsha 410082, Peoples R China

[3] Univ Technol Sydney, Fac Engn Informat Technol, Ctr Artificial Intelligence, Ultimo, NSW 2007, Australia

[4] Texas A&M Univ Qatar, Sci Program, POB 23874, Doha, Qatar

来源：

NEURAL NETWORKS | 2023年 / 166卷

基金：

中国国家自然科学基金;

关键词：

Synchronization; Switched neural networks; Stochastic disturbances; Stochastic impulses; State-dependent switching; GLOBAL EXPONENTIAL SYNCHRONIZATION; TIME-VARYING DELAYS; COMPLEX NETWORKS; DYNAMICAL NETWORKS; ROBUST STABILITY; SYSTEMS; NODES;

D O I：

10.1016/j.neunet.2023.07.045

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 theoretical analysis on exponential synchronization of a class of coupled switched neural networks suffering from stochastic disturbances and impulses is presented. A control law is developed and two sets of sufficient conditions are derived for the synchronization of coupled switched neural networks. First, for desynchronizing stochastic impulses, the synchronization of coupled switched neural networks is analyzed by Lyapunov function method, the comparison principle and a impulsive delay differential inequality. Then, for general stochastic impulses, by partitioning impulse interval and using the convex combination technique, a set of sufficient condition on the basis of linear matrix inequalities (LMIs) is derived for the synchronization of coupled switched neural networks. Eventually, two numerical examples and a practical application are elaborated to illustrate the effectiveness of the theoretical results.& COPY; 2023 Elsevier Ltd. All rights reserved.

引用

页码：459 / 470

页数：12

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