Exponential stability analysis for neural networks with time-varying delay

Min Wu, Fang Liu, Peng Shi, Yong He, Ryuichi Yokoyama

Research output: Contribution to journalArticle

82 Citations (Scopus)

Abstract

This correspondence paper focuses on the problem of exponential stability for neural networks with a time-varying delay. The relationship among the time-varying delay, its upper bound, and their difference is taken into account. As a result, an improved linear-matrix-inequality-based delay-dependent exponential stability criterion is obtained without ignoring any terms in the derivative of Lyapunov-Krasovskii functional. Two numerical examples are given to demonstrate its effectiveness.

Original languageEnglish
Pages (from-to)1152-1156
Number of pages5
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume38
Issue number4
DOIs
Publication statusPublished - 2008 Aug

Fingerprint

Asymptotic stability
Neural networks
Stability criteria
Linear matrix inequalities
Derivatives

Keywords

  • Exponential stability
  • Linear matrix inequality (LMI)
  • Neural networks
  • Time-varying delay

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Human-Computer Interaction
  • Medicine(all)

Cite this

Exponential stability analysis for neural networks with time-varying delay. / Wu, Min; Liu, Fang; Shi, Peng; He, Yong; Yokoyama, Ryuichi.

In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 38, No. 4, 08.2008, p. 1152-1156.

Research output: Contribution to journalArticle

Wu, Min ; Liu, Fang ; Shi, Peng ; He, Yong ; Yokoyama, Ryuichi. / Exponential stability analysis for neural networks with time-varying delay. In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 2008 ; Vol. 38, No. 4. pp. 1152-1156.
@article{ffb80985a3d74dc3867050c9a8379a3f,
title = "Exponential stability analysis for neural networks with time-varying delay",
abstract = "This correspondence paper focuses on the problem of exponential stability for neural networks with a time-varying delay. The relationship among the time-varying delay, its upper bound, and their difference is taken into account. As a result, an improved linear-matrix-inequality-based delay-dependent exponential stability criterion is obtained without ignoring any terms in the derivative of Lyapunov-Krasovskii functional. Two numerical examples are given to demonstrate its effectiveness.",
keywords = "Exponential stability, Linear matrix inequality (LMI), Neural networks, Time-varying delay",
author = "Min Wu and Fang Liu and Peng Shi and Yong He and Ryuichi Yokoyama",
year = "2008",
month = "8",
doi = "10.1109/TSMCB.2008.915652",
language = "English",
volume = "38",
pages = "1152--1156",
journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics",
issn = "1083-4419",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

TY - JOUR

T1 - Exponential stability analysis for neural networks with time-varying delay

AU - Wu, Min

AU - Liu, Fang

AU - Shi, Peng

AU - He, Yong

AU - Yokoyama, Ryuichi

PY - 2008/8

Y1 - 2008/8

N2 - This correspondence paper focuses on the problem of exponential stability for neural networks with a time-varying delay. The relationship among the time-varying delay, its upper bound, and their difference is taken into account. As a result, an improved linear-matrix-inequality-based delay-dependent exponential stability criterion is obtained without ignoring any terms in the derivative of Lyapunov-Krasovskii functional. Two numerical examples are given to demonstrate its effectiveness.

AB - This correspondence paper focuses on the problem of exponential stability for neural networks with a time-varying delay. The relationship among the time-varying delay, its upper bound, and their difference is taken into account. As a result, an improved linear-matrix-inequality-based delay-dependent exponential stability criterion is obtained without ignoring any terms in the derivative of Lyapunov-Krasovskii functional. Two numerical examples are given to demonstrate its effectiveness.

KW - Exponential stability

KW - Linear matrix inequality (LMI)

KW - Neural networks

KW - Time-varying delay

UR - http://www.scopus.com/inward/record.url?scp=49049108880&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=49049108880&partnerID=8YFLogxK

U2 - 10.1109/TSMCB.2008.915652

DO - 10.1109/TSMCB.2008.915652

M3 - Article

VL - 38

SP - 1152

EP - 1156

JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

SN - 1083-4419

IS - 4

ER -