Adaptive quasi-synchronization analysis for Caputo delayed Cohen-Grossberg neural networks

被引：10

作者：

Zhang, Hai ^{[1
]}

Chen, Xinbin ^{[1
]}

Ye, Renyu ^{[1
]}

Stamova, Ivanka ^{[2
]}

Cao, Jinde ^{[3
,4
]}

机构：

[1] Anqing Normal Univ, Sch Math & Phys, Anqing 246133, Peoples R China

[2] Univ Texas San Antonio, Dept Math, San Antonio, TX 78249 USA

[3] Southeast Univ, Sch Math, Nanjing 211189, Peoples R China

[4] Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea

来源：

MATHEMATICS AND COMPUTERS IN SIMULATION | 2023年 / 212卷

基金：

中国国家自然科学基金;

关键词：

Caputo derivative; Adaptive control; Cohen-Grossberg neural networks; Quasi-synchronization; Laplace transform; FIXED-TIME SYNCHRONIZATION; STABILITY; FEEDBACK;

D O I：

10.1016/j.matcom.2023.04.025

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The quasi-synchronization (QS) issues for Caputo delayed Cohen-Grossberg neural networks (CGNNs) are discussed in this article. To begin with, a novel lemma is established by constructing suitable fractional differential inequality. Due to the advantages of adaptive control schemes with reducing control cost and having high tracking accuracy, two different adaptive controllers are designed, respectively. Applying the proposed lemma, inequality techniques and Lagrange's mean value theorem, the conditions of QS are obtained by selecting appropriate Lyapunov functions. Finally, two numerical examples in different dimensions are shown to test the correctness of the gained theorems. (c) 2023 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

引用

页码：49 / 65

页数：17

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