New stability results for delayed neural networks

被引：32
|
作者：
Shao, Hanyong [1 ]
Li, Huanhuan [1 ]
Zhu, Chuanjie [1 ]
机构：
[1] Qufu Normal Univ, Res Inst Automat, Rizhao 276826, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2017年 / 311卷
基金：
中国国家自然科学基金;
关键词：
Neural networks; Lyapunov-Krasovskii functional; Integral inequality; Asymptotic stability; DEPENDENT STABILITY; EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; CRITERIA; STABILIZATION;
D O I：
10.1016/j.amc.2017.05.023
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper is concerned with the stability for delayed neural networks. By more fully making use of the information of the activation function, a new Lyapunov-Krasovskii functional (LKF) is constructed. Then a new integral inequality is developed, and more information of the activation function is taken into account when the derivative of the LKF is estimated. By Lyapunov stability theory, a new stability result is obtained. Finally, three examples are given to illustrate the stability result is less conservative than some recently reported ones. (C) 2017 Elsevier Inc. All rights reserved.
引用
收藏
页码：324 / 334
页数：11
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