Global exponential periodicity and global exponential stability of a class of recurrent neural networks
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 36-48 |
Journal / Publication | Physics Letters, Section A: General, Atomic and Solid State Physics |
Volume | 329 |
Issue number | 1-2 |
Publication status | Published - 16 Aug 2004 |
Externally published | Yes |
Link(s)
Abstract
Some sufficient criteria have been given ensuring existence, uniqueness and global exponential stability of periodic solution of a class of recurrent neural network (RNN) model by using the comparison principle, the theory of monotone flow and monotone operator. The conditions are very viable in some applied fields. For instance, they can be applied to design globally exponentially stable RNNs and periodic oscillatory RNNs and easily checked in practice. In addition, we provide a new and efficacious method for the qualitative analysis of neural networks. © 2004 Elsevier B.V.
Research Area(s)
- 43.80.+p, 85.40.Ls, 87.10.+e
Citation Format(s)
Global exponential periodicity and global exponential stability of a class of recurrent neural networks. / Chen, Boshan; Wang, Jun.
In: Physics Letters, Section A: General, Atomic and Solid State Physics, Vol. 329, No. 1-2, 16.08.2004, p. 36-48.
In: Physics Letters, Section A: General, Atomic and Solid State Physics, Vol. 329, No. 1-2, 16.08.2004, p. 36-48.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review