Stability analysis of delayed neural networks based on a relaxed delay-product-type Lyapunov functional

被引：27
|
作者：
Chen, Yun [1 ,2 ]
Chen, Gang [3 ]
机构：
[1] Hunan City Univ, Sch Mech & Elect Engn, Yiyang 413002, Peoples R China
[2] Key Lab Energy Monitoring & Edge Comp Smart City, Yiyang 413002, Peoples R China
[3] Hunan Univ Technol, Sch Elect & Informat Engn, Zhuzhou 412007, Peoples R China
来源：
NEUROCOMPUTING | 2021年 / 439卷
基金：
中国国家自然科学基金;
关键词：
Delayed neural networks; Stability; Polynomial inequalities; Lyapunov-Krasovskii functional; Time-varying delay; TIME-VARYING DELAY; POLYNOMIAL INEQUALITIES; ASYMPTOTIC STABILITY; STATE ESTIMATION; SYSTEMS; CRITERIA;
D O I：
10.1016/j.neucom.2021.01.098
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 deals with the stability analysis of delayed neural networks. A necessary and sufficient condition for positivity or negativity of the high-order polynomial over a finite interval is derived. An appropriate Lyapunov-Krasovskii functional (LKF), including a relax delay-product-type Lyapunov functional, is constructed. The necessary and sufficient condition of the polynomial inequality and a relaxed delay-product-type Lyapunov functional are employed to derive less conservative stability criteria. Finally, three commonly used numerical examples are presented to demonstrate the effectiveness and less conservativeness of the proposed method. (C) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页码：340 / 347
页数：8
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