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 a 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 of fully supervised training and we conclude that Online training suppose a reduction in the number of iterations and therefore increase the speed of convergence.
This research was supported by the project MAPACI TIC2002-02273 of CICYT in Spain.
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Fernández-Redondo, M., Hernández-Espinosa, C., Ortiz-Gómez, M., Torres-Sospedra, J. (2004). Gradient Descent Training of Radial Basis Functions. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_39
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DOI: https://doi.org/10.1007/978-3-540-28647-9_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
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