Published October 2007 | Version v1
Journal article

Delay-dependent global stability results for delayed Hopfield neural networks

  • 1. Liaoning Key Lab of Intelligent Information Processing, Dalian University, Dalian 116622 (China)

Description

In this paper, by utilizing Lyapunov functional method and the linear matrix inequality approach, we analyze the global asymptotic stability of Hopfield neural networks with time delays. A new sufficient condition ensuring global asymptotic stability of the unique equilibrium point of delayed Hopfield neural networks is obtained. The result is related to the size of delays. A numerical example is given to illustrate the efficiency of our result

Additional details

Identifiers

DOI
10.1016/j.chaos.2006.03.073;
PII
S0960-0779(06)00310-9;

Publishing Information

Journal Title
Chaos, Solitons and Fractals
Journal Volume
34
Journal Issue
2
Journal Page Range
p. 662-668
ISSN
0960-0779

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
39005808
Subject category
S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
Descriptors DEI
EFFICIENCY; EQUILIBRIUM; LYAPUNOV METHOD; MATRICES; NEURAL NETWORKS; STABILITY; TIME DELAY
Descriptors DEC
CALCULATION METHODS

Optional Information

Copyright
Copyright (c) 2006 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.