Average Impulsive Weight Based Event-Triggered Impulsive Synchronization on Coupled Neural Networks

被引：14

作者：

Tang, Ze ^{[1
]}

Jiang, Chenhui ^{[1
]}

Wang, Yan ^{[1
]}

Feng, Jianwen ^{[2
]}

Park, Ju H. H. ^{[3
]}

机构：

[1] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China

[2] Shenzhen Univ, Coll Math & Stat, Shenzhen 518060, Peoples R China

[3] Yeungnam Univ, Dept Elect Engn, Kyonsan 38541, South Korea

来源：

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING | 2023年 / 10卷 / 04期

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

Average impulsive weight; event-trigger protocol; hybrid impulses; neural networks; poisson noise; STOCHASTIC SYNCHRONIZATION; QUASI-SYNCHRONIZATION; LAG SYNCHRONIZATION; STABILITY ANALYSIS; COMPLEX NETWORKS; SYSTEMS; DELAY;

D O I：

10.1109/TNSE.2023.3243248

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This article explores the stochastic synchronization for a class of coupled neural networks through a novel event-triggered impulsive control strategy. In view of hybrid impulses in the controller, the desynchronizing and synchronizing impulses are both discussed in consideration of the average impulsive weight. The triggering conditions are presented according to the latest impulsive weight and the overall impulsive weight, respectively and an exponential threshold function is correspondingly proposed to explain the triggering mechanism. Sufficient conditions for the synchronization are successfully obtained with jointly utilizing the mathematical induction methodology and the variation of parameter formula. In addition, the convergence velocity of the coupled neural networks is precisely estimated considering the different delayed impulsive comparison systems. In addition, the Zeno behaviors could be successfully eliminated with the proposed event-triggered function. Finally, one numerical example is presented to validate the results.

引用

页码：2180 / 2189

页数：10

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