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.
Idioma original | Inglés |
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Páginas (desde-hasta) | 2829-2833 |
Número de páginas | 5 |
Publicación | Proceedings of the American Control Conference |
Volumen | 4 |
Estado | Publicada - 1999 |
Publicado de forma externa | Sí |
Evento | Proceedings of the 1999 American Control Conference (99ACC) - San Diego, CA, USA Duración: 2 jun. 1999 → 4 jun. 1999 |