Stability criterion for delayed neural networks via Wirtinger-based multiple integral inequality

被引：36

作者：

Ding, Sanbo ^{[1
]}

Wang, Zhanshan ^{[1
]}

Wu, Yanming ^{[1
]}

Zhang, Huaguang ^{[1
]}

机构：

[1] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110819, Peoples R China

来源：

NEUROCOMPUTING | 2016年 / 214卷

基金：

中国国家自然科学基金;

关键词：

Delayed neural networks; Wirtinger-based multiple integral inequality; Global asymptotic stability; Lyapunov-Krasovskii functional with multiple integral terms; TIME-VARYING DELAYS; SYSTEMS;

D O I：

10.1016/j.neucom.2016.04.058

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This brief provides an alternative way to reduce the conservativeness of the stability criterion for neural networks (NNs) with time-varying delays. The core is that a series of multiple integral terms are considered as a part of the Lyapunov-Krasovskii functional (LKF). In order to estimate the multiple integral terms in the derivative of the LKF, a multiple integral inequality, named Wirtinger-based multiple integral inequality (WMII), is proposed. This inequality includes some recent related results as its special cases. Based on the multiple integral forms of LKF and the WMII, a novel delay dependent stability criterion for NNs with time-varying delays is derived. The effectiveness of the established stability criterion is verified by an open example. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：53 / 60

页数：8

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