| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 408013 | Neurocomputing | 2011 | 5 Pages |
Abstract
By employing Lyapunov functional theory as well as linear matrix inequalities, ultimate boundedness of stochastic Hopfield neural networks (HNN) with time-varying delays is investigated. Sufficient criteria on ultimate boundedness of stochastic HNN are firstly obtained, which fills up a gap and includes deterministic systems as our special case. Finally, numerical simulations are presented to illustrate the correctness and effectiveness of our theoretical results.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Li Wan, Qinghua Zhou, Pei Wang,