Abstract
Stability properties of recurrent neural networks are investigated using Lyapunov stability theory and functional analytic means. Sufficient conditions for the global asymptotic stability and exponentially asymptotic stability of equilibrium points of a class of recurrent neural networks are provided. The results obtained can be applied when recurrent neural networks are used as computation models, in particular, as optimization models. The results may also be used as stability analysis tools for some class of nonlinear control systems.
| Original language | English (US) |
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| Title of host publication | Proceedings of the American Control Conference |
| Publisher | American Automatic Control Council |
| Pages | 3346-3350 |
| Number of pages | 5 |
| Volume | 3 |
| State | Published - 1994 |
| Event | Proceedings of the 1994 American Control Conference. Part 1 (of 3) - Baltimore, MD, USA Duration: Jun 29 1994 → Jul 1 1994 |
Other
| Other | Proceedings of the 1994 American Control Conference. Part 1 (of 3) |
|---|---|
| City | Baltimore, MD, USA |
| Period | 6/29/94 → 7/1/94 |
ASJC Scopus subject areas
- Control and Systems Engineering