Training guidelines for neural networks to estimate stability regions
Resumen:
This paper presents new results on the use of neural networks to estimate stability regions for autonomous nonlinear systems. In contrast to model-based analytical methods, this approach uses empirical data from the system to train the neural network. A method is developed to generate confidence intervals for the regions identified by the trained neural network. The neural network results are compared with estimates obtained by previously proposed methods for a standard two-dimensional example.
| 1999 | |
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Neural networks Power system stability State estimation Linear systems Chemical reactors Nonlinear systems |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/20778 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución – No Comercial – Sin Derivadas (CC - By-NC-ND) |
| Sumario: | Trabajo presentado a American Control Conference 1999. |
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