| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1892476 | Chaos, Solitons & Fractals | 2006 | 6 Pages |
Abstract
Based on the Lyapunov functional stability analysis for differential equations and the linear matrix inequality (LMI) optimization approach, a new delay-dependent criterion for neural networks with discrete and distributed delays is derived to guarantee global asymptotic stability. The criterion is expressed in terms of LMIs, which can be solved easily by various convex optimization algorithms. Some numerical examples are given to show the effectiveness of proposed method.
Related Topics
Physical Sciences and Engineering
Physics and Astronomy
Statistical and Nonlinear Physics
Authors
Ju H. Park,