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. © Springer-Verlag 2004.
CITATION STYLE
Fernández-Redondo, M., Hernández-Espinosa, C., Ortiz-Gómez, M., & Torres-Sospedra, J. (2004). Gradient descent training of radial basis functions. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3173, 229–234. https://doi.org/10.1007/978-3-540-28647-9_39
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