Published November 2004
| Version v1
Journal article
Globally exponentially robust stability and periodicity of delayed neural networks
Creators
Description
In this paper, a new concept of robust periodicity is introduced, and problem of robust stability and robust periodicity is discussed for delayed neural networks. Several sufficient conditions are derived for globally exponentially robust stability and robust periodicity of delayed neural networks based Lyapunov method. These results improve and extend those given in the earlier references
Additional details
Identifiers
- DOI
- 10.1016/j.chaos.2004.03.019;
- PII
- S0960077904001791;
Publishing Information
- Journal Title
- Chaos, Solitons and Fractals
- Journal Volume
- 22
- Journal Issue
- 4
- Journal Page Range
- p. 957-963
- ISSN
- 0960-0779
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 35056126
- Subject category
- S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
- Descriptors DEI
- LYAPUNOV METHOD; NEURAL NETWORKS; PERIODICITY; STABILITY
- Descriptors DEC
- CALCULATION METHODS; VARIATIONS
Optional Information
- Copyright
- Copyright (c) 2004 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.