Delay-dependent stability criteria for time-varying delay neural networks in the delta domain

被引：13

作者：

Yuan, Yuan ^{[1
]}

Sun, Fuchun ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Comp Sci & Technol, Tsinghua Natl Lab Informat Sci & Technol, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China

来源：

NEUROCOMPUTING | 2014年 / 125卷

基金：

高等学校博士学科点专项科研基金;

关键词：

Neural networks; Time-varying delay; Delta operator; Delay-dependent stability; EXPONENTIAL STABILITY; ROBUST STABILITY; VARIABLE DELAYS; SYSTEMS;

D O I：

10.1016/j.neucom.2012.09.040

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the delay-dependent stability criterion for time-varying delay neural networks in the delta domain is investigated. The unified neural networks, which can be used in both continues-time space and discrete-time space, takes advantage with a high sampling frequency. In the framework of the newly proposed neural networks, the delay-dependent stability criteria is derived in terms of linear matrix inequality by constructing the Lyapunov-Krasovskii function in the delta domain. A numerical simulation is given to show the effectiveness and superiority of the proposed approach. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.

引用

页码：17 / 21

页数：5

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