Global Exponential Stability of a General Class of Recurrent Neural Networks with Time-Varying Delays
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 22_Publication in policy or professional journal
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 1353-1358 |
| Journal / Publication | IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications |
| Volume | 50 |
| Issue number | 10 |
| Publication status | Published - Oct 2003 |
| Externally published | Yes |
Link(s)
Abstract
This brief presents new theoretical results on the global exponential stability of neural networks with time-varying delays and Lipschitz continuous activation functions. These results include several sufficient conditions for the global exponential stability of general neural networks with time-varying delays and without monotone, bounded, or continuously differentiable activation function. In addition to providing new criteria for neural networks with time-varying delays, these stability conditions also improve upon the existing ones with constant time delays and without time delays. Furthermore, it is convenient to estimate the exponential convergence rates of the neural networks by using the results.
Research Area(s)
- Global exponential stability, Neural networks, Rate of exponential convergence, Time-varying delays
Citation Format(s)
Global Exponential Stability of a General Class of Recurrent Neural Networks with Time-Varying Delays. / Zeng, Zhigang; Wang, Jun; Liao, Xiaoxin.
In: IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, Vol. 50, No. 10, 10.2003, p. 1353-1358.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 22_Publication in policy or professional journal
