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CONVERGENCE OF DISCRETE-TIME RECURRENT NEURAL NETWORKS WITH VARIABLE DELAY.
- Source :
- International Journal of Bifurcation & Chaos in Applied Sciences & Engineering; Feb2005, Vol. 15 Issue 2, p581-595, 15p
- Publication Year :
- 2005
-
Abstract
- In this paper, some global exponential stability criteria for the equilibrium point of discrete-time recurrent neural networks with variable delay are presented by using the linear matrix inequality (LMI) approach. The neural networks considered are assumed to have asymmetric weighting matrices throughout this paper. On the other hand, by applying matrix decomposition, the model is embedded into a cooperative one, the latter possesses important order-preserving properties which are basic to our analysis. A sufficient condition is obtained ensuring the componentwise exponential stability of the system with specific performances such as decay rate and trajectory bounds. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02181274
- Volume :
- 15
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- International Journal of Bifurcation & Chaos in Applied Sciences & Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 16978673
- Full Text :
- https://doi.org/10.1142/S0218127405012235