New stability results for delayed neural networks with data packet dropouts

被引：10

作者：

Cai, Xiao ^{[1
]}

Zhong, Shouming ^{[1
]}

Wang, Jun ^{[2
]}

Shi, Kaibo ^{[3
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Peoples R China

[2] Southwest Minzu Univ, Coll Elect & Informat Engn, Chengdu 610041, Peoples R China

[3] Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Peoples R China

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2020年 / 555卷

基金：

中国国家自然科学基金;

关键词：

Neural networks; Data packet dropouts; Time-varying delay; Integral inequality; DEPENDENT EXPONENTIAL STABILITY; GLOBAL ASYMPTOTIC STABILITY; TIME-VARYING DELAYS; CRITERIA; INEQUALITY; SYSTEMS;

D O I：

10.1016/j.physa.2020.124727

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

This paper further investigates the stability analysis of delayed neural networks (DNNs) with data packet dropouts. Firstly, the delay-product-type function method is introduced to construct a suitable Lyapunov-Krasovskii functional (LKF) with delay-dependent matrices, which fully considers the integral terms, non-integral terms and time-delay correlation terms. Then, by applying free-matrix-base inequality (FMBI) and other valid inequalities mathematical analysis techniques, new stability criteria are established. Meanwhile, by solving a set of linear matrix inequalities (LMIs), the corresponding controllers are designed to ensure the system state stabilization. Finally, two examples are given to demonstrate the validity and feasibility of the proposed method. (C) 2020 Elsevier B.V. All rights reserved.

引用

页数：15

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