Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction

Ryad Zemouri, Daniel Racoceanu, Noureddine Zerhouni, Eugenia Minca, Florin Filip

Producción científica: Contribución a una revistaArtículo de la conferenciarevisión exhaustiva

4 Citas (Scopus)

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 originalInglés
Páginas (desde-hasta)85-90
Número de páginas6
PublicaciónAIP Conference Proceedings
Volumen1107
DOI
EstadoPublicada - 2009
Publicado de forma externa
Evento2nd Mediterranean Conference on Intelligent Systems and Automation, CISA 2009 - Zarzis, Túnez
Duración: 23 mar. 200925 mar. 2009

Huella

Profundice en los temas de investigación de 'Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction'. En conjunto forman una huella única.

Citar esto