A new stability criterion of stochastic neural networks with delays

Yun Chen; Wei Xing Zheng

doi:10.1109/CDC.2012.6426757

A new stability criterion of stochastic neural networks with delays

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

Abstract

![CDATA[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.]]

Original language	English
Title of host publication	51st IEEE Conference on Decision and Control: December 10-13, 2012, Maui, Hawaii, USA
Publisher	IEEE
Pages	5386-5391
Number of pages	6
ISBN (Print)	9781467320641
DOIs	https://doi.org/10.1109/CDC.2012.6426757
Publication status	Published - 2012
Event	IEEE Conference on Decision & Control - Duration: 10 Dec 2012 → …

Publication series

Name
ISSN (Print)	0743-1546

Conference

Conference	IEEE Conference on Decision & Control
Period	10/12/12 → …

Keywords

neural networks (computer science)
stability
stochastic noise
time-varying delays

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10.1109/CDC.2012.6426757

Cite this

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title = "A new stability criterion of stochastic neural networks with delays",

abstract = "![CDATA[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 = "neural networks (computer science), stability, stochastic noise, time-varying delays",

author = "Yun Chen and Zheng, {Wei Xing}",

year = "2012",

doi = "10.1109/CDC.2012.6426757",

language = "English",

isbn = "9781467320641",

publisher = "IEEE",

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booktitle = "51st IEEE Conference on Decision and Control: December 10-13, 2012, Maui, Hawaii, USA",

note = "IEEE Conference on Decision & Control ; Conference date: 10-12-2012",

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TY - GEN

T1 - A new stability criterion of stochastic neural networks with delays

AU - Chen, Yun

AU - Zheng, Wei Xing

PY - 2012

Y1 - 2012

N2 - ![CDATA[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.]]

AB - ![CDATA[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.]]

KW - neural networks (computer science)

KW - stability

KW - stochastic noise

KW - time-varying delays

UR - http://handle.uws.edu.au:8081/1959.7/521033

UR - http://control.disp.uniroma2.it/cdc2012/

U2 - 10.1109/CDC.2012.6426757

DO - 10.1109/CDC.2012.6426757

M3 - Conference Paper

SN - 9781467320641

SP - 5386

EP - 5391

BT - 51st IEEE Conference on Decision and Control: December 10-13, 2012, Maui, Hawaii, USA

PB - IEEE

T2 - IEEE Conference on Decision & Control

Y2 - 10 December 2012

ER -

A new stability criterion of stochastic neural networks with delays

Abstract

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Keywords

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