Abstract
By combining Cohen-Grossberg neural networks with an arbitrary switching rule, the mathematical model of a class of switched Cohen-Grossberg neural networks with mixed time-varying delays is established. Moreover, robust stability for such switched Cohen-Grossberg neural networks is analyzed based on a Lyapunov approach and linear matrix inequality (LMI) technique. Simple sufficient conditions are given to guarantee the switched Cohen-Grossberg neural networks to be globally asymptotically stable for all admissible parametric uncertainties. The proposed LMI-based results are computationally efficient as they can be solved numerically using standard commercial software. An example is given to illustrate the usefulness of the results. © 2006 IEEE.
| Original language | English |
|---|---|
| Pages (from-to) | 1356-1363 |
| Journal | IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics |
| Volume | 36 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - Dec 2006 |
Research Keywords
- Cohen-Grossberg neural networks
- Mixed time-varying delays
- Robust stability
- Switched systems
- Uncertain systems
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