A new stability criterion of stochastic neural networks with delays

Author

Yun Chen ; Wei Xing Zheng

Author_Institution

Inst. of Inf. & Control, Hangzhou Dianzi Univ., Hangzhou, China

fYear

2012

fDate

10-13 Dec. 2012

Firstpage

5386

Lastpage

5391

Abstract

This paper investigates the problem of mean-square asymptotic stability of uncertain neural networks with time-varying delay and stochastic noise. Based on generalized Finsler lemma and the linear matrix inequality (LMI) optimization technique, an improved delay-dependent stability criterion is developed. It is shown that the new stability criterion is less conservative and less computationally complex than the existing stability conditions. A numerical example is presented to substantiate the effectiveness of the theoretical results.

Keywords

asymptotic stability; delays; linear matrix inequalities; neural nets; stability criteria; stochastic programming; time-varying systems; uncertain systems; LMI optimization technique; delay-dependent stability criterion; generalized Finsler lemma; linear matrix inequality; mean-square asymptotic stability problem; stability conditions; stochastic neural networks; stochastic noise; time-varying delay; uncertain neural networks; Artificial neural networks; Asymptotic stability; Delay; Noise; Numerical stability; Stability criteria; Symmetric matrices;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control (CDC), 2012 IEEE 51st Annual Conference on

Conference_Location

Maui, HI

ISSN

0743-1546

Print_ISBN

978-1-4673-2065-8

Electronic_ISBN

0743-1546

Type

conf

DOI

10.1109/CDC.2012.6426757

Filename

6426757