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Sequential learning algorithm for PG-RBF network using regression weights for time series prediction

  • Plasticity Phenomena (Maturing, Learning & Memory)
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Foundations and Tools for Neural Modeling (IWANN 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1606))

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Abstract

We propose a modified radial basis function network (RBF) in which the main characteristics are that: a) the gaussian function is modified using pseudo-gaussian (PG) in which two scaling parameters σ are introduced; b) the activation of the hidden neurons is normalized c) instead of using a single parameter for the output weights, these are functions of the input variables; d) a sequential learning algorithm is presented to adapt the structure of the network, in which it is possible to create a new hidden unit and also to detect and remove inactive units. It is shown that the modified PG-RBF can reduce the number of didden units significantly compared with the classical RBF network. The feasibility of the resulting algorithm for the neural network to evolve and learn is demonstrated by predicting time series.

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Authors and Affiliations

  1. Department of Architecture and Computer Technology, University of Granada, Spain

    I. Rojas, H. Pomares, Juris L. Bernier, Juris Ortega, E. Ros & A. Prieto

Authors
  1. I. Rojas
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  2. H. Pomares
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  3. Juris L. Bernier
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  4. Juris Ortega
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  5. E. Ros
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  6. A. Prieto
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Editor information

José Mira Juan V. Sánchez-Andrés

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© 1999 Springer-Verlag Berlin Heidelberg

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Rojas, I., Pomares, H., Bernier, J.L., Ortega, J., Ros, E., Prieto, A. (1999). Sequential learning algorithm for PG-RBF network using regression weights for time series prediction. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098221

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  • DOI: https://doi.org/10.1007/BFb0098221

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66069-9

  • Online ISBN: 978-3-540-48771-5

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