Leakage delay-dependent stability analysis for complex-valued neural networks with discrete and distributed time-varying delays

被引：44

作者：

Samidurai, R. ^{[1
]}

Sriraman, R. ^{[1
]}

Zhu, Song ^{[2
]}

机构：

[1] Thiruvalluvar Univ, Dept Math, Vellore 632115, Tamil Nadu, India

[2] China Univ Min & Technol, Sch Math, Xuzhou 221116, Jiangsu, Peoples R China

来源：

NEUROCOMPUTING | 2019年 / 338卷

关键词：

Complex-valued neural networks; Lyapunov-Krasovskii functional; Integral inequality; Leakage delay; EXPONENTIAL STABILITY; PASSIVITY ANALYSIS; STATE ESTIMATION; SYSTEMS; SYNCHRONIZATION; CRITERION;

D O I：

10.1016/j.neucom.2019.02.027

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper investigates the leakage delay-dependent global asymptotic stability problem for a class of complex-valued neural networks (CVNNs) with discrete and distributed time-varying delays. In order to handle this issue easily, an appropriate Lyapunov-Krasovskii functional (LKF) is constructed with some augmented delay-dependent terms. By employing integral inequalities, several delay-dependent sufficient conditions are derived that ensure the global asymptotic stability of the considered system model. Moreover, the results obtained in this paper have expressed in terms of complex-valued linear matrix inequalities (LMIs), whose feasible solutions can be easily verified by effective YALMIP control toolbox in MATLAB LMI. Finally, two benchmark illustrative examples are given to show the effectiveness and advantages of the proposed results. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：262 / 273

页数：12

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