Switching synchronization of reaction-diffusion neural networks with time-varying delays

被引：8
|
作者：
Hu, Dandan [1 ]
Tan, Jieqing [1 ,2 ]
Shi, Kaibo [3 ]
Ding, Kui [4 ]
机构：
[1] Hefei Univ Technol, Sch Math, Hefei 230009, Peoples R China
[2] Hefei Univ Technol, Sch Comp & Informat, Hefei 230009, Peoples R China
[3] Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Peoples R China
[4] Hunan Normal Univ, Sch Math & Stat, MOE LCSM, Changsha 410081, Peoples R China
来源：
CHAOS SOLITONS & FRACTALS | 2022年 / 155卷
基金：
中国国家自然科学基金;
关键词：
Switching reaction-diffusion neural; networks; Synchronization; Switching-time-dependent Lyapunov; functional; function; Switching law strategies; Time-varying delays; STABILITY ANALYSIS; STABILIZATION; SYSTEMS;
D O I：
10.1016/j.chaos.2021.111766
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
This paper explores the switching synchronization problem of reaction-diffusion neural networks with time-varying delays, and two improved synchronization switching law strategies are proposed for stability analysis. One is constructed by adopting a Lyapunov-Krasovskii functional combined with the use of improved Wirtinger's integral inequality for managing the reaction-diffusion terms. The other is designed to utilize the Lyapunov-Razumikhin function, which is easier to deal with the reaction-diffusion terms directly compared to the former one. As a result, the time-space feature of the proposed switching synchronization is more robust and compatible than previous works. Finally, the simulated numerical experiments make out the effectiveness of the developed approaches in this work.(c) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页数：12
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