New Delay-Dependent Stability Criteria for Neural Networks With Two Additive Time-Varying Delay Components

被引：95

作者：

Shao, Hanyong ^{[1
,2
]}

Han, Qing-Long ^{[2
,3
]}

机构：

[1] Qufu Normal Univ, Sch Elect & Informat Automat, Rizhao 276826, Shandong, Peoples R China

[2] Cent Queensland Univ, Ctr Intelligent & Networked Syst, Rockhampton, Qld 4702, Australia

[3] Cent Queensland Univ, Sch Informat & Commun Technol, Rockhampton, Qld 4702, Australia

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2011年 / 22卷 / 05期

基金：

中国国家自然科学基金; 澳大利亚研究理事会;

关键词：

Lyapunov functional; neural networks; stability; time-varying delays; GLOBAL ROBUST STABILITY; ASYMPTOTIC STABILITY;

D O I：

10.1109/TNN.2011.2114366

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 is concerned with delay-dependent stability for neural networks with two additive time-varying delay components. By constructing a new Lyapunov functional and using a convex polyhedron method to estimate the derivative of the Lyapunov functional, some new delay-dependent stability criteria are derived. These stability criteria are less conservative than some existing ones. An example is given to demonstrate the less conservatism of the stability results.

引用

页码：812 / 818

页数：7

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