Global exponential convergence of Cohen-Grossberg neural networks with time delays

Hongtao Lu; Ruiming Shen; Fu Lai Korris Chung

doi:10.1109/TNN.2005.853336

Global exponential convergence of Cohen-Grossberg neural networks with time delays

Hongtao Lu, Ruiming Shen, Fu Lai Korris Chung

Research output: Journal article publication › Journal article › Academic research › peer-review

40 Citations (Scopus)

Abstract

In this paper, we derive a general sufficient condition ensuring global exponential convergence of Cohen-Grossberg neural networks with time delays by constructing a novel Lyapunov functional and smartly estimating its derivative. The proposed condition is related to the convex combinations of the column-sum and the row-sum of the connection matrices and also relaxes the constraints on the network coefficients. Therefore, the proposed condition generalizes some previous results in the literature.

Original language	English
Pages (from-to)	1694-1696
Number of pages	3
Journal	IEEE Transactions on Neural Networks
Volume	16
Issue number	6
DOIs	https://doi.org/10.1109/TNN.2005.853336
Publication status	Published - 1 Nov 2005

Keywords

Cohen-Grossberg neural networks
Global exponential stability
Time delay

ASJC Scopus subject areas

Control and Systems Engineering
Theoretical Computer Science
Electrical and Electronic Engineering
Artificial Intelligence
Computational Theory and Mathematics
Hardware and Architecture

More information

10.1109/TNN.2005.853336

Cite this

@article{a197a44d0bae48ee993acf8e9070c338,

title = "Global exponential convergence of Cohen-Grossberg neural networks with time delays",

abstract = "In this paper, we derive a general sufficient condition ensuring global exponential convergence of Cohen-Grossberg neural networks with time delays by constructing a novel Lyapunov functional and smartly estimating its derivative. The proposed condition is related to the convex combinations of the column-sum and the row-sum of the connection matrices and also relaxes the constraints on the network coefficients. Therefore, the proposed condition generalizes some previous results in the literature.",

keywords = "Cohen-Grossberg neural networks, Global exponential stability, Time delay",

author = "Hongtao Lu and Ruiming Shen and Chung, {Fu Lai Korris}",

year = "2005",

month = nov,

day = "1",

doi = "10.1109/TNN.2005.853336",

language = "English",

volume = "16",

pages = "1694--1696",

journal = "IEEE Transactions on Neural Networks",

issn = "1045-9227",

publisher = "IEEE Computational Intelligence Society",

number = "6",

}

TY - JOUR

T1 - Global exponential convergence of Cohen-Grossberg neural networks with time delays

AU - Lu, Hongtao

AU - Shen, Ruiming

AU - Chung, Fu Lai Korris

PY - 2005/11/1

Y1 - 2005/11/1

N2 - In this paper, we derive a general sufficient condition ensuring global exponential convergence of Cohen-Grossberg neural networks with time delays by constructing a novel Lyapunov functional and smartly estimating its derivative. The proposed condition is related to the convex combinations of the column-sum and the row-sum of the connection matrices and also relaxes the constraints on the network coefficients. Therefore, the proposed condition generalizes some previous results in the literature.

AB - In this paper, we derive a general sufficient condition ensuring global exponential convergence of Cohen-Grossberg neural networks with time delays by constructing a novel Lyapunov functional and smartly estimating its derivative. The proposed condition is related to the convex combinations of the column-sum and the row-sum of the connection matrices and also relaxes the constraints on the network coefficients. Therefore, the proposed condition generalizes some previous results in the literature.

KW - Cohen-Grossberg neural networks

KW - Global exponential stability

KW - Time delay

UR - http://www.scopus.com/inward/record.url?scp=28244449615&partnerID=8YFLogxK

U2 - 10.1109/TNN.2005.853336

DO - 10.1109/TNN.2005.853336

M3 - Journal article

SN - 1045-9227

VL - 16

SP - 1694

EP - 1696

JO - IEEE Transactions on Neural Networks

JF - IEEE Transactions on Neural Networks

IS - 6

ER -

Global exponential convergence of Cohen-Grossberg neural networks with time delays

Abstract

Keywords

ASJC Scopus subject areas

More information

Other files and links

Fingerprint

Cite this