Parallel Computation of an Adaptive Optimal RBF Network Predictor

  • Authors:

  • M. Salmerón;J. Ortega;C.G. Puntonet;M. Damas

  • Affiliations:

  • Department of Computer Architecture and Computer Technology, University of Granada. ETS Ingeniería Informática, GRANADA, Spain E-18071;Department of Computer Architecture and Computer Technology, University of Granada. ETS Ingeniería Informática, GRANADA, Spain E-18071;Department of Computer Architecture and Computer Technology, University of Granada. ETS Ingeniería Informática, GRANADA, Spain E-18071;Department of Computer Architecture and Computer Technology, University of Granada. ETS Ingeniería Informática, GRANADA, Spain E-18071

  • Venue:
  • IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
  • Year:
  • 2009

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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.