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Some Experiments on Training Radial Basis Functions by Gradient Descent

  • Mercedes Fernández-Redondo
  • Carlos Hernández-Espinosa
  • Mamen Ortiz-Gómez
  • Joaquíin Torres-Sospedra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3316)

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 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 some 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 leads to a reduction in the number of iterations and therefore increase the speed of convergence.

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References

  1. 1.
    Moody, J., Darken, C.J.: Fast Learning in Networks of Locally-Tuned Procesing Units. Neural Computation 1, 281–294 (1989)CrossRefGoogle Scholar
  2. 2.
    Roy, A., Govil, S., et al.: A Neural-Network Learning Theory and Polynomial Time RBF Algorithm. IEEE Trans. on Neural Networks 8(6), 1301–1313 (1997)CrossRefGoogle Scholar
  3. 3.
    Hwang, Y., Bang, S.: An Efficient Method to Construct a Radial Basis Function Neural Network Classifier. Neural Network 10(8), 1495–1503 (1997)CrossRefGoogle Scholar
  4. 4.
    Roy, A., Govil, S., et al.: An Algorithm to Generate Radial Basis Function (RBF)- LikeNets for Classification Problems. Neural Networks 8(2), 179–201 (1995)CrossRefGoogle Scholar
  5. 5.
    Krayiannis, N.: Reformulated Radial Basis Neural Networks Trained by Gradient Descent. IEEE Trans. on Neural Networks 10(3), 657–671 (1999)CrossRefGoogle Scholar
  6. 6.
    Krayiannis, N., Randolph-Gips, M.: On the Construction and Training of Reformulated Radial Basis Functions. IEEE Trans. Neural Networks 14(4), 835–846 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Mercedes Fernández-Redondo
    • 1
  • Carlos Hernández-Espinosa
    • 1
  • Mamen Ortiz-Gómez
    • 1
  • Joaquíin Torres-Sospedra
    • 1
  1. 1.Universidad Jaume ID. de Ingeniería y Ciencia de los ComputadoresCastellónSpain

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