Resumen
In this paper, we present a training technique of a Recurrent Radial Basis Function neural network for fault prediction. We use the Fuzzy Min-Max technique to initialize the k-center of the RRBF neural network. The k-means algorithm is then applied to calculate the centers that minimize the mean square error of the prediction task. The performances of the k-means algorithm are then boosted by the Fuzzy Min-Max technique.
Idioma original | Inglés |
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Páginas (desde-hasta) | 85-90 |
Número de páginas | 6 |
Publicación | AIP Conference Proceedings |
Volumen | 1107 |
DOI | |
Estado | Publicada - 2009 |
Publicado de forma externa | Sí |
Evento | 2nd Mediterranean Conference on Intelligent Systems and Automation, CISA 2009 - Zarzis, Túnez Duración: 23 mar. 2009 → 25 mar. 2009 |