Published March 14, 2005
| Version v1
Journal article
Global exponential stability and periodicity of cellular neural networks with variable delays
Creators
- 1. Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China) and Department of Mathematics, Xinjiang Normal University, Urumqi 830054 (China)
Description
The Letter presents sufficient conditions ensuring the global exponential stability and existence of the periodic solution for cellular neural networks with variable delays. The results allow for the consideration of all unbounded neuron activation functions (but not necessarily surjective), in particular, can analyze the exponential stability and periodicity for the linear cellular neural networks. The work provides one such method which can be applied to cellular neural networks systems with variable delays. The method, based on the theory of fixed point and differential inequality technique. The applicability of the present results is demonstrated by two examples
Additional details
Identifiers
- DOI
- 10.1016/j.physleta.2004.12.001;
- PII
- S0375-9601(04)01675-5;
Publishing Information
- Journal Title
- Physics Letters. A
- Journal Volume
- 336
- Journal Issue
- 4-5
- Journal Page Range
- p. 331-341
- ISSN
- 0375-9601
- CODEN
- PYLAAG
INIS
- Country of Publication
- Netherlands
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 37033871
- Subject category
- S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
- Descriptors DEI
- FUNCTIONS; MATHEMATICAL SOLUTIONS; NERVE CELLS; NEURAL NETWORKS; PERIODICITY; STABILITY; TIME DELAY
- Descriptors DEC
- ANIMAL CELLS; SOMATIC CELLS; VARIATIONS
Optional Information
- Copyright
- Copyright (c) 2004 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.