Synchronization of Time-Varying Delayed Neural Networks by Fixed-Time Control

被引：18

作者：

Xu, Yuhua ^{[1
,2
]}

Wu, Xiaoqun ^{[3
,4
]}

Xu, Chao ^{[5
]}

机构：

[1] Nanjing Audit Univ, Sch Finance, Nanjing 211815, Jiangsu, Peoples R China

[2] Nanjing Audit Univ, Jiangsu Key Lab Financial Engn, Nanjing 211815, Jiangsu, Peoples R China

[3] Wuhan Univ, Sch Math & Stat, Wuhan 430072, Hubei, Peoples R China

[4] Wuhan Univ, Hubei Key Lab Computat Sci, Wuhan 430072, Hubei, Peoples R China

[5] Nanjing Audit Univ, Sch Informat Engn, Nanjing 211815, Jiangsu, Peoples R China

来源：

IEEE ACCESS | 2018年 / 6卷

基金：

中国国家自然科学基金;

关键词：

Neural network; synchronization; fixed-time control; the convergence time; CONSENSUS; DYNAMICS; TRACKING; SYSTEMS;

D O I：

10.1109/ACCESS.2018.2883417

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper discusses synchronization of time-varying delayed neural networks by fixed-time control. First, several new fixed-time stability theorems of the dynamic system are discussed, and the estimation of the convergence time is also gained. Compared with some existing results, the convergence time given in this paper can be less conservative and more accurate. Second, as one of the important applications of fixed-time stability, several novel sufficient criteria are derived such that the two time-varying neural networks can be synchronized within a fixed-time. Finally, the simulation result is presented to show the effectiveness of the theoretical result.

引用

页码：74240 / 74246

页数：7

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