Published October 2007
| Version v1
Journal article
Delay-dependent global stability results for delayed Hopfield neural networks
Creators
- 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.