Global exponential stabilization of neural networks with time delay via impulsive control

Wu-Hua Chen; Xiaomei Lu; Wei Xing Zheng

doi:10.1109/CDC.2014.7040454

Global exponential stabilization of neural networks with time delay via impulsive control

Wu-Hua Chen, Xiaomei Lu, Wei Xing Zheng

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

Abstract

![CDATA[The problem of global exponential stabilization of discrete-time delayed neural networks (DDNNs) via impulsive control is addressed in this paper. A novel time-varying Lyapunov functional is proposed to capture the dynamical characteristic of discrete-time impulsive delayed neural networks (DIDNNs). In conjunction with the convex combination technique, new conditions in the form of linear matrix inequalities are established for global exponential stability of DIDNNs. The distinct features of the new stability conditions for DIDNNs are that they are dependent upon the lengths of impulsive intervals but independent of the size of time delay. This paves the way for designing the impulsive controller for impulsive stabilization of DDNNs. The applicability of the developed global exponential stabilization conditions is validated by numerical results.]]

Original language	English
Title of host publication	Proceedings of the 53rd IEEE Conference on Decision and Control, December 15-17, 2014, Los Angeles, USA
Publisher	IEEE
Pages	6782-6787
Number of pages	6
ISBN (Print)	9781467360883
DOIs	https://doi.org/10.1109/CDC.2014.7040454
Publication status	Published - 2014
Event	IEEE Conference on Decision & Control - Duration: 15 Dec 2014 → …

Conference

Conference	IEEE Conference on Decision & Control
Period	15/12/14 → …

Keywords

control theory
discrete-time systems
neural networks (computer science)

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10.1109/CDC.2014.7040454

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@inproceedings{def60b3daa8443148d3c07af45547b56,

title = "Global exponential stabilization of neural networks with time delay via impulsive control",

abstract = "![CDATA[The problem of global exponential stabilization of discrete-time delayed neural networks (DDNNs) via impulsive control is addressed in this paper. A novel time-varying Lyapunov functional is proposed to capture the dynamical characteristic of discrete-time impulsive delayed neural networks (DIDNNs). In conjunction with the convex combination technique, new conditions in the form of linear matrix inequalities are established for global exponential stability of DIDNNs. The distinct features of the new stability conditions for DIDNNs are that they are dependent upon the lengths of impulsive intervals but independent of the size of time delay. This paves the way for designing the impulsive controller for impulsive stabilization of DDNNs. The applicability of the developed global exponential stabilization conditions is validated by numerical results.]]",

keywords = "control theory, discrete-time systems, neural networks (computer science)",

author = "Wu-Hua Chen and Xiaomei Lu and Zheng, {Wei Xing}",

year = "2014",

doi = "10.1109/CDC.2014.7040454",

language = "English",

isbn = "9781467360883",

pages = "6782--6787",

booktitle = "Proceedings of the 53rd IEEE Conference on Decision and Control, December 15-17, 2014, Los Angeles, USA",

publisher = "IEEE",

note = "IEEE Conference on Decision & Control ; Conference date: 15-12-2014",

}

TY - GEN

T1 - Global exponential stabilization of neural networks with time delay via impulsive control

AU - Chen, Wu-Hua

AU - Lu, Xiaomei

AU - Zheng, Wei Xing

PY - 2014

Y1 - 2014

N2 - ![CDATA[The problem of global exponential stabilization of discrete-time delayed neural networks (DDNNs) via impulsive control is addressed in this paper. A novel time-varying Lyapunov functional is proposed to capture the dynamical characteristic of discrete-time impulsive delayed neural networks (DIDNNs). In conjunction with the convex combination technique, new conditions in the form of linear matrix inequalities are established for global exponential stability of DIDNNs. The distinct features of the new stability conditions for DIDNNs are that they are dependent upon the lengths of impulsive intervals but independent of the size of time delay. This paves the way for designing the impulsive controller for impulsive stabilization of DDNNs. The applicability of the developed global exponential stabilization conditions is validated by numerical results.]]

AB - ![CDATA[The problem of global exponential stabilization of discrete-time delayed neural networks (DDNNs) via impulsive control is addressed in this paper. A novel time-varying Lyapunov functional is proposed to capture the dynamical characteristic of discrete-time impulsive delayed neural networks (DIDNNs). In conjunction with the convex combination technique, new conditions in the form of linear matrix inequalities are established for global exponential stability of DIDNNs. The distinct features of the new stability conditions for DIDNNs are that they are dependent upon the lengths of impulsive intervals but independent of the size of time delay. This paves the way for designing the impulsive controller for impulsive stabilization of DDNNs. The applicability of the developed global exponential stabilization conditions is validated by numerical results.]]

KW - control theory

KW - discrete-time systems

KW - neural networks (computer science)

UR - http://handle.uws.edu.au:8081/1959.7/uws:29269

U2 - 10.1109/CDC.2014.7040454

DO - 10.1109/CDC.2014.7040454

M3 - Conference Paper

SN - 9781467360883

SP - 6782

EP - 6787

BT - Proceedings of the 53rd IEEE Conference on Decision and Control, December 15-17, 2014, Los Angeles, USA

PB - IEEE

T2 - IEEE Conference on Decision & Control

Y2 - 15 December 2014

ER -

Global exponential stabilization of neural networks with time delay via impulsive control

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