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Parallel Computation of an Adaptive Optimal RBF Network Predictor

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Artificial Neural Nets Problem Solving Methods (IWANN 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2687))

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Abstract

In this paper we analyze parallel processing in clusters of computers of an improved prediction method based on RBF neural networks and matrix decomposition techniques (SVD and QR-cp). Parallel processing is required because of the extensive computation found in sucn an hybrid prediction technique, the reward being better prediction performance and also less network complexity. We discuss two alternatives of concurrency: parallel implementation of the prediction procedure over the ScaLAPACK suite, and the formulation of another parallel routine customized to a higher degree for better performance in the case of the QR-cp procedure.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Architecture and Computer Technology, University of Granada. ETS Ingeniería Informática, Campus Aynadamar s/n, E-18071, GRANADA, Spain

    M. Salmerón, J. Ortega, C.G. Puntonet & M. Damas

Authors
  1. M. Salmerón
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  2. J. Ortega
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  3. C.G. Puntonet
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  4. M. Damas
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Editor information

Editors and Affiliations

  1. E.T.S. de Ingeniería Informática Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia, Juan del Rosal, 16, 28040, Madrid, Spain

    José Mira  & José R. Álvarez  & 

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© 2003 Springer-Verlag Berlin Heidelberg

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Salmerón, M., Ortega, J., Puntonet, C., Damas, M. (2003). Parallel Computation of an Adaptive Optimal RBF Network Predictor. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_54

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  • DOI: https://doi.org/10.1007/3-540-44869-1_54

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40211-4

  • Online ISBN: 978-3-540-44869-3

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