Multiple finite-time synchronization and settling-time estimation of delayed competitive neural networks

被引：4
|
作者：
Wang, Leimin [1 ,2 ,3 ]
Tan, Xingxing [1 ,2 ,3 ]
Wang, Qingyi [1 ,2 ,3 ]
Hu, Junhao [4 ]
机构：
[1] China Univ Geosci, Sch Automat, Wuhan 430074, Peoples R China
[2] Hubei Key Lab Adv Control & Intelligent Automat Co, Wuhan 430074, Peoples R China
[3] Minist Educ, Engn Res Ctr Intelligent Technol Geoexplorat, Wuhan 430074, Peoples R China
[4] South Cent Univ Nationalities, Coll Math & Stat, Wuhan 430074, Peoples R China
来源：
NEUROCOMPUTING | 2023年 / 552卷
基金：
中国国家自然科学基金;
关键词：
A unified control framework; Finite-time synchronization; Fixed-time synchronization; Preassigned-time synchronization; Delayed competitive neural networks; STABILIZATION; STABILITY; FRAMEWORK; SYSTEMS; ORDER;
D O I：
10.1016/j.neucom.2023.126555
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Finite-time synchronization and its settling-time estimation has been a hot topic in fields of science and engineering. This paper proposes a unified control framework to study the multiple finite-time synchronization of delayed competitive neural networks (DCNNs). Firstly, a more comprehensive finite-time framework lemma is systematically established. Compared with existing finite-time control method, the proposed framework involves several kinds of synchronization results and it enhances the estimation of settling-time. Then, based on the control framework, the finite-time, fixed-time and preassigned-time synchronization of DCNNs can be achieved simultaneously by modifying the controller parameters. Finally, numerical example and image encryption application are presented to demonstrate the validity and superiority of the deduced results.
引用
收藏
页数：14
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