An experimental study on training radial basis functions by gradient descent

Authors:
Joaquín Torres-Sospedra;Carlos Hernández-Espinosa;Mercedes Fernández-Redondo
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:
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Year:
2006

Citing 6
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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 consist 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.