Gradient descent and radial basis functions

Authors:
Mercedes Fernández-Redondo;Joaquín Torres-Sospedra;Carlos Hernández-Espinosa
Affiliations:
Departamento de Ingenieria y Ciencia de los Computadores, Universitat Jaume I, Castellon, Spain;Departamento de Ingenieria y Ciencia de los Computadores, Universitat Jaume I, Castellon, Spain;Departamento de Ingenieria y Ciencia de los Computadores, Universitat Jaume I, Castellon, Spain
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

Citing 6
Cited 0

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Abstract

In this paper, we present experiments comparing different training algorithms for Radial Basis Functions (RBF) neural networks. In particular we compare the classical training which consists of an unsupervised training of centers followed by a supervised training of the weights at the output, with the full supervised training by gradient descent proposed recently in same papers. We conclude that a fully supervised training performs generally better. We also compare Batch training with Online training and we conclude that Online training suppose a reduction in the number of iterations.