Global robust exponential stability analysis for delayed recurrent neural networks

被引：0

作者：

Zhang, Zhizhou ^{[1
]}

Zhang, Lingling ^{[2
]}

She, Longhua ^{[1
]}

Huang, Lihong ^{[2
]}

机构：

[1] Natl Univ Def Technol, Dept Mech Engn & Automat, Changcha, Hunan, Peoples R China

[2] Hunan Univ, Dept Math & Econometr, Changsha, Hunan, Peoples R China

来源：

2008 INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION, VOLS 1-4 | 2008年

基金：

中国国家自然科学基金;

关键词：

Delayed recurrent neural networks; Global exponential stability; Interval systems; Linear matrix inequality;

D O I：

10.1109/ICINFA.2008.4608051

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper provides a new sufficient condition for the global robust exponential stability of a delayed recurrent neural network. The conditions are expressed in terms of LMIs, which can be easily checked by various recently developed algorithms in solving convex optimization problems. Examples are provided to demonstrate the reduced conservatism of the proposed exponential stability condition.

引用

页码：499 / +

页数：2

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