Abstract
The problem of global exponential stability analysis of Impulsive high-order Hopfield-type neural networks with time-varying delays and reaction-diffusion terms has been investigated in this paper. Using the Lyapunov function method and M-matrix theory, we establish the global exponential stability of the neural networks with its estimated exponential convergence rate. As an illustration, a numerical example is given using the results. © 2011 Taylor & Francis.
| Original language | English |
|---|---|
| Pages (from-to) | 3150-3162 |
| Journal | International Journal of Computer Mathematics |
| Volume | 88 |
| Issue number | 15 |
| DOIs | |
| Publication status | Published - Oct 2011 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to <a href="mailto:[email protected]">[email protected]</a>.Funding
This work was partially supported by the Fundamental Research Funds for the Central Universities of China (Project Nos. CDJXS10 18 00 16, CDJZR10 18 55 01) and the National Natural Science Foundation of China (Grant No. 60974020).
Research Keywords
- delay
- exponential stability
- high-order Hopfield-type neural networks
- impulse
- reaction-diffusion
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