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Improved Conditions for Global Exponential Stability of Recurrent Neural Networks With Time-Varying Delays.
- Source :
-
IEEE Transactions on Neural Networks . May2006, Vol. 17 Issue 3, p623-636. 14p. 3 Graphs. - Publication Year :
- 2006
-
Abstract
- This paper presents new theoretical results on global exponential stability of recurrent neural networks with bounded activation functions and time-varying delays. The stability conditions depend on external inputs, connection weights, and time delays of recurrent neural networks. Using these results, the global exponential stability of recurrent neural networks can be derived, and the estimated location of the equilibrium point can be obtained. As typical representatives, the Hopfield neural network (HNN) and the cellular neural network (CNN) are examined in detail. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10459227
- Volume :
- 17
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks
- Publication Type :
- Academic Journal
- Accession number :
- 20955154
- Full Text :
- https://doi.org/10.1109/TNN.2006.873283