Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
838571 | Nonlinear Analysis: Real World Applications | 2009 | 9 Pages |
Abstract
This note provides new results on global asymptotic stability for neural networks with time-varying delay. Two types of time-varying delay are considered: one is differentiable and has bounded derivative; the other one is continuous and may vary very fast. By introducing an augmented Lyapunov–Krasovskii functional, new delay-dependent stability criteria for delayed neural networks are derived in terms of linear matrix inequalities (LMIs). It is shown that the obtained criteria can provide less conservative results than some existing ones. Numerical examples are given to demonstrate the applicability of the proposed approach.
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Authors
Tao Li, Lei Guo, Chong Lin, Changyin Sun,