Published April 13, 2009
| Version v1
Journal article
Improved result on stability analysis of discrete stochastic neural networks with time delay
Creators
- 1. National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Yuquan Campus, Hangzhou 310027 (China)
- 2. College of Information Science and Technology, Donghua University, Shanghai 200051 (China)
Description
This Letter investigates the problem of exponential stability for discrete stochastic time-delay neural networks. By defining a novel Lyapunov functional, an improved delay-dependent exponential stability criterion is established in terms of linear matrix inequality (LMI) approach. Meanwhile, the computational complexity of the newly established stability condition is reduced because less variables are involved. Numerical example is given to illustrate the effectiveness and the benefits of the proposed method.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.physleta.2009.02.056Additional details
Identifiers
- DOI
- 10.1016/j.physleta.2009.02.056;
- PII
- S0375-9601(09)00243-6;
Publishing Information
- Journal Title
- Physics Letters. A
- Journal Volume
- 373
- Journal Issue
- 17
- Journal Page Range
- p. 1546-1552
- ISSN
- 0375-9601
- CODEN
- PYLAAG
INIS
- Country of Publication
- Netherlands
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 41059491
- Subject category
- S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
- Descriptors DEI
- LYAPUNOV METHOD; MATRICES; NEURAL NETWORKS; STABILITY; STOCHASTIC PROCESSES; TIME DELAY
- Descriptors DEC
- CALCULATION METHODS
Optional Information
- Copyright
- Copyright (c) 2009 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.