Abstract
In this paper, we investigate the problem of global exponential stability analysis for a class of bidirectional associative memory (BAM) neural networks with interval time-delays. Improved exponential stability condition is derived by employing new Lyapunov-Krasovskii functional and the integral inequality. Several special cases of interest are derived. The developed stability criteria are delay dependent and characterized by linear matrix inequalities (LMIs). The developed results are shown to be less conservative than previous published ones in the literature. Finally, simulations of two numerical examples are provided to demonstrate the efficacy of our approach.
| Original language | English |
|---|---|
| Pages (from-to) | 284-290 |
| Number of pages | 7 |
| Journal | Neurocomputing |
| Volume | 74 |
| Issue number | 1-3 |
| DOIs | |
| State | Published - Dec 2010 |
Keywords
- BAM neural networks
- Global exponential stability
- Interval time-delays
- LMIs
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence
Fingerprint
Dive into the research topics of 'LMI-based exponential stability criterion for bidirectional associative memory neural networks'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver