Global exponential stability and periodic solutions of recurrent neural networks with delays
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) | 393-404 |
Journal / Publication | Physics Letters, Section A: General, Atomic and Solid State Physics |
Volume | 298 |
Issue number | 5-6 |
Publication status | Published - 17 Jun 2002 |
Externally published | Yes |
Link(s)
Abstract
In this Letter, by utilizing the Lyapunov functional method, applying M-matrix and topological degree theory, we analyze the global exponential stability and the existence of periodic solutions of a class of recurrent neural networks with delays. Some simple and new sufficient conditions ensuring existence, uniqueness and global exponential stability of the equilibrium point and periodic solutions of delayed recurrent neural networks are obtained, which do not require the activation functions to be differentiable, bounded and monotone nondecreasing. In addition, two examples are also given to illustrate the theory. © 2002 Elsevier Science B.V. All rights reserved.
Research Area(s)
- Delays, Global exponential stability, Lyapunov function, M-matrix, Periodic solutions, Recurrent neural networks, Topological degree
Citation Format(s)
Global exponential stability and periodic solutions of recurrent neural networks with delays. / Huang, He; Cao, Jinde; Wang, Jun.
In: Physics Letters, Section A: General, Atomic and Solid State Physics, Vol. 298, No. 5-6, 17.06.2002, p. 393-404.
In: Physics Letters, Section A: General, Atomic and Solid State Physics, Vol. 298, No. 5-6, 17.06.2002, p. 393-404.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review