Global Exponential Stability Analysis for Recurrent Neural Networks with Time-varying Delay

被引：0

作者：

Guo, Xiaoli ^{[1
]}

Li, Qingbo ^{[1
]}

Chen, Yonggang ^{[2
]}

Wu, Yuanyuan ^{[3
]}

机构：

[1] Zhengzhou Univ Light Ind, Dept Math & Informat Sci, Zhengzhou 450002, Peoples R China

[2] Henan Inst Sci Technol, Dept Math, Xinxiang 453003, Peoples R China

[3] Southeast Univ, Sch Automat, Nanjing 210096, Peoples R China

来源：

CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS | 2009年

关键词：

Static neural networks; Time-varying delay; Global exponential stability; Linear matrix inequalities (LMIs); ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; DEPENDENT STABILITY; LMI APPROACH; DISCRETE; CRITERIA;

D O I：

暂无

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 letter deals with the exponential stability problem for static recurrent neural networks (RNNs) with time-varying delay. By Lyapunov functional method and linear matrix inequality technique, some novel delay-dependent criteria are established to ensure the exponential stability of the considered neural network. The proposed exponential stability criteria are expressed in terms of linear matrix inequalities, and can be checked using the recently developed algorithms. A numerical example is given to show that the obtained criteria can provide less conservative results than some existing ones.

引用

页码：2976 / +

页数：2

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