Regional sampled-data synchronization of chaotic neural networks using piecewise-continuous delay dependent Lyapunov functional

被引：10

作者：

Han, S. Y. ^{[1
]}

Kommuri, S. K. ^{[2
]}

Kwon, O. M. ^{[3
]}

Lee, S. M. ^{[1
]}

机构：

[1] Kyungpook Natl Univ, Sch Elect & Elect Engn, Cyber Phys Syst & Control Lab, Daehak Ro 80, Daegu 41566, South Korea

[2] Xian Jiaotong Liverpool Univ, Dept Elect & Elect Engn, Suzhou 215123, Peoples R China

[3] Chungbuk Natl Univ, Sch Elect Engn, 1 Chungdae Ro, Cheongju 28644, South Korea

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2022年 / 423卷

基金：

新加坡国家研究基金会;

关键词：

Chaotic neural networks; Synchronization; Piecewise-continuous delay dependent; Lyapunov functional; Sum of squares; DATA CONTROLLER-DESIGN; TIME-VARYING DELAY; STABILITY ANALYSIS; LINEAR-SYSTEMS; INPUT SATURATION; ANTIWINDUP; LTI;

D O I：

10.1016/j.amc.2022.126994

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, a regional sampled-data synchronization criterion is proposed for the chaotic neural networks (CNNs) with input saturation using the piecewise-continuous delay dependent Lyapunov functional (PDDLF) approach. The aim of this work is to enlarge the region of attraction (ROA) of the synchronous state for CNNs with input saturation. Unlike existing works, the Lyapunov functional in the proposed approach is constructed from a polynomial with respect to the piecewise-continuous delay. Moreover, the proposed Lyapunov functional is combined with looped-functionals to derive the sufficient condition. The synchronization criterion is formulated in terms of sum of squares (SOS) programs, which reduces the infinite-dimensional linear matrix inequality (LMI) conditions to a finite number of SOS conditions. A numerical example is presented to illustrate the effectiveness and advantages of the proposed approach.(c) 2022 Elsevier Inc. All rights reserved.

引用

页数：15

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