Global Asymptotic Stability for a Class of Generalized Neural Networks with Interval Time-Varying Delays

被引：229

作者：

Zhang, Xian-Ming ^{[1
,2
]}

Han, Qing-Long ^{[1
,2
]}

机构：

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

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

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2011年 / 22卷 / 08期

基金：

澳大利亚研究理事会;

关键词：

Generalized neural networks; global asymptotic stability; interval time-varying delays; local field neural networks; static neural networks; EXPONENTIAL STABILITY; DEPENDENT STABILITY; LINEAR-SYSTEMS; ROBUST STABILITY; CRITERION; STATE; DISCRETE; MODELS;

D O I：

10.1109/TNN.2011.2147331

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 is concerned with global asymptotic stability for a class of generalized neural networks (NNs) with interval time-varying delays, which include two classes of fundamental NNs, i.e., static neural networks (SNNs) and local field neural networks (LFNNs), as their special cases. Some novel delay-independent and delay-dependent stability criteria are derived. These stability criteria are applicable not only to SNNs but also to LFNNs. It is theoretically proven that these stability criteria are more effective than some existing ones either for SNNs or for LFNNs, which is confirmed by some numerical examples.

引用

页码：1180 / 1192

页数：13

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