Abstract
A global stability analysis of a particular class of recurrent neural networks with time-varying delay is performed. Both Lipschitz continuous and monotone non-decreasing activation functions are considered. Globally asymptotically delay-dependent stability criteria are derived in the form of linear matrix inequalities through the use of Leibniz-Newton formula and relaxation matrices. Finally, two numerical examples are given to illustrate the effectiveness of the given criterion.
| Original language | English |
|---|---|
| Pages (from-to) | 736-742 |
| Number of pages | 7 |
| Journal | IET Control Theory and Applications |
| Volume | 2 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2008 Jul 24 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Human-Computer Interaction
- Computer Science Applications
- Control and Optimization
- Electrical and Electronic Engineering
Cite this
Lu, C. Y., Su, T. J., & Huang, S. C. (2008). Delay-dependent stability analysis for recurrent neural networks with time-varying delay. IET Control Theory and Applications, 2(8), 736-742. https://doi.org/10.1049/iet-cta:20070313