Fixed-time synchronization of discontinuous competitive neural networks with time-varying delays

被引：46

作者：

Zheng, Caicai ^{[1
]}

Hu, Cheng ^{[1
]}

Yu, Juan ^{[1
]}

Jiang, Haijun ^{[1
]}

机构：

[1] Xinjiang Univ, Coll Math & Syst Sci, Urumqi 830017, Peoples R China

来源：

NEURAL NETWORKS | 2022年 / 153卷

基金：

中国国家自然科学基金;

关键词：

Competitive neural network; Fixed-time synchronization; Discontinuous activation; Time-varying delay; GLOBAL EXPONENTIAL STABILITY; OUTER SYNCHRONIZATION; COMPLEX NETWORKS; SAMPLED-DATA;

D O I：

10.1016/j.neunet.2022.06.002

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 article, the fixed-time (FXT) synchronization of discontinuous competitive neural networks (CNNs) involving time-varying delays is investigated. Firstly, two kinds of discontinuous FXT control schemes are proposed and two forms of Lyapunov function are constructed based on p-norm and 1-norm to discuss the FXT synchronization of CNNs. By means of nonsmooth analysis and some inequality techniques, some simple criteria are obtained to achieve FXT synchronization and the upper bound of the settling time with less conservativeness is provided. Furthermore, the effect of time scale on FXT synchronization of CNNs is considered. Lastly, some numerical results for an example are provided to demonstrate the derived theoretical results. (c) 2022 Elsevier Ltd. All rights reserved.

引用

页码：192 / 203

页数：12

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