SSA, SVD, QR-cp, and RBF Model Reduction (original) (raw)

Abstract

We propose an application of SVD model reduction to the class of RBF neural models for improving performance in contexts such as on-line prediction of time series. The SVD is coupled with QR-cp factorization. It has been found that such a coupling leads to more precise extraction of the relevant information, even when using it in an heuristic way. Singular Spectrum Analysis (SSA) and its relation to our method is also mentioned. We analize performance of the proposed on-line algorithm using a ‘benchmark’ chaotic time series and a difficult-to-predict, dynamically changing series.

Research partially supported by the Spanish MCyT Projects TIC2001-2845, TIC2000-1348 and DPI2001-3219.

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Authors and Affiliations

  1. Department of Computer Architecture and Computer Technology, University of Granada. E.T.S. Ingeniería Informática, Campus Aynadamar s/n., E-18071, Granada, Spain
    Moisés Salmerón, Julio Ortega, Carlos García Puntonet, Alberto Prieto & Ignacio Rojas

Authors

  1. Moisés Salmerón
  2. Julio Ortega
  3. Carlos García Puntonet
  4. Alberto Prieto
  5. Ignacio Rojas

Editor information

Editors and Affiliations

  1. ETS Informática, Universidad Autónoma de Madrid, 28049, Madrid, Spain
    José R. Dorronsoro

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© 2002 Springer-Verlag Berlin Heidelberg

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Salmerón, M., Ortega, J., Puntonet, C.G., Prieto, A., Rojas, I. (2002). SSA, SVD, QR-cp, and RBF Model Reduction. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5\_96

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