随机Hopfield时滞神经网络均方指数稳定性 : LMI方法

Wuhua Chen; Xiaomei Lu; Qunhong Li; Zhihong Guan

随机Hopfield时滞神经网络均方指数稳定性 : LMI方法

Translated title of the contribution: Exponential stability in mean square for stochastic Hopfield delay neural networks : an LMI approach

Wuhua Chen
, Xiaomei Lu
, Qunhong Li
, Zhihong Guan

Research output: Contribution to journal › Article › peer-review

Abstract

By using a technique of model transformation of the system, a new type of Lyapunov functional is introduced. By applying this new Lyapunov functional, a novel delay-dependent sufficient condition of exponential stability in mean square for stochastic Hopfield delay neural networks is derived in terms of linear matrix inequalities (LMIs). A delay-independent sufficient condition is also presented. Numerical examples show that the proposed method is less conservative than the previous ones.

Translated title of the contribution	Exponential stability in mean square for stochastic Hopfield delay neural networks : an LMI approach
Original language	Chinese (Simplified)
Pages (from-to)	109-117
Number of pages	9
Journal	Acta Mathematica Scientia. Series A
Volume	27
Issue number	1
Publication status	Published - 2007

Cite this

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title = "随机Hopfield时滞神经网络均方指数稳定性 : LMI方法",

abstract = "By using a technique of model transformation of the system, a new type of Lyapunov functional is introduced. By applying this new Lyapunov functional, a novel delay-dependent sufficient condition of exponential stability in mean square for stochastic Hopfield delay neural networks is derived in terms of linear matrix inequalities (LMIs). A delay-independent sufficient condition is also presented. Numerical examples show that the proposed method is less conservative than the previous ones.",

author = "Wuhua Chen and Xiaomei Lu and Qunhong Li and Zhihong Guan",

year = "2007",

language = "Chinese (Simplified)",

volume = "27",

pages = "109--117",

journal = "Acta Mathematica Scientia. Series A",

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publisher = "Springer",

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T1 - 随机Hopfield时滞神经网络均方指数稳定性 : LMI方法

AU - Chen, Wuhua

AU - Lu, Xiaomei

AU - Li, Qunhong

AU - Guan, Zhihong

PY - 2007

Y1 - 2007

N2 - By using a technique of model transformation of the system, a new type of Lyapunov functional is introduced. By applying this new Lyapunov functional, a novel delay-dependent sufficient condition of exponential stability in mean square for stochastic Hopfield delay neural networks is derived in terms of linear matrix inequalities (LMIs). A delay-independent sufficient condition is also presented. Numerical examples show that the proposed method is less conservative than the previous ones.

AB - By using a technique of model transformation of the system, a new type of Lyapunov functional is introduced. By applying this new Lyapunov functional, a novel delay-dependent sufficient condition of exponential stability in mean square for stochastic Hopfield delay neural networks is derived in terms of linear matrix inequalities (LMIs). A delay-independent sufficient condition is also presented. Numerical examples show that the proposed method is less conservative than the previous ones.

UR - http://handle.uws.edu.au:8081/1959.7/533175

M3 - Article

SN - 1003-3998

VL - 27

SP - 109

EP - 117

JO - Acta Mathematica Scientia. Series A

JF - Acta Mathematica Scientia. Series A

IS - 1

ER -

随机Hopfield时滞神经网络均方指数稳定性 : LMI方法

Abstract

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