Mean-square finite-time synchronization of stochastic competitive neural networks with infinite time-varying delays and reaction-diffusion terms

被引：9
|
作者：
Xu, Chenguang [1 ,2 ]
Jiang, Minghui [1 ,2 ]
Hu, Junhao [3 ]
机构：
[1] China Three Gorges Univ, Three Gorges Math Res Ctr, Yichang, Hubei, Peoples R China
[2] China Three Gorges Univ, Inst Nonlinear Complex Syst, Yichang 443000, Hubei, Peoples R China
[3] South Cent Univ Nationalities, Coll Math & Stat, Wuhan 430074, Hubei, Peoples R China
来源：
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION | 2023年 / 127卷
基金：
中国国家自然科学基金;
关键词：
Mean-square finite-time synchronization; Reaction-diffusion; Stochastic competitive neural networks; Adaptive control; Infinite delay; EXPONENTIAL SYNCHRONIZATION; STABILITY ANALYSIS; LEAKAGE;
D O I：
10.1016/j.cnsns.2023.107535
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper focuses on the mean-square finite-time synchronization (MFTS) of stochastic com-petitive neural networks with infinite time-varying discrete delays and reaction-diffusion terms (IRSCNNs). Different from other articles, this paper presents a new approach, which does not use finite-time stability theorem but uses integral inequality, Gronwall-type inequality and comparison strategy to study MFTS. In addition, two control schemes are designed: a feedback control scheme and a new adaptive control strategy, which can achieve MFTS of IRSCNNs. Finally, the correctness of the results is verified by examples.
引用
收藏
页数：18
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