Abstract
In this paper, the problem of global exponential stability for a class of neural networks with interval time-varying delay is investigated. The time-delay pattern is quite general and including fast time-varyings. It is assumed that the time delay belongs to a given interval, but the derivative of a time-varying delay be less than 1 is removed, or the delay function is not necessary to be differentiable. By constructing a set of improved Lyapunov-Krasovskii functionals combined with a known integral inequality, new delay-dependent exponential stability criteria with explicitly exponential convergence rate are established in terms of LMIs (linear matrix inequalities). The stability criteria are less conservative than the existing results in the literatures. Numerical examples are given to illustrate the effectiveness of the results.
| Original language | English |
|---|---|
| Pages (from-to) | 357-363 |
| Number of pages | 7 |
| Journal | Neurocomputing |
| Volume | 97 |
| DOIs | |
| State | Published - 15 Nov 2012 |
Bibliographical note
Funding Information:The authors would like to thank the reviewers and the Editor-in-Chief for their constructive comments that help improving the revised manuscript.The research of the first two-authors is partially supported by the Funds of USTB and NNSF of China ( 11071013 ). The third author is supported by the Deanship of Scientific Research (DSR) at KFUPM through research group project no. RG-1105-1 .
Keywords
- Global exponential stability
- Interval time-varying delay
- Linear matrix inequalities
- Lyapunov-Krasovskii functional
- Neural networks
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence
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