• DocumentCode
    3499669
  • Title

    Forecasting time series with a logarithmic model for the Polynomial Artificial Neural Networks

  • Author

    Luna-Sanchez, J.C. ; Gomez-Ramirez, E. ; Najim, K. ; Ikonen, E.

  • Author_Institution
    Intell. Syst. Group, La Salle Univ., Mexico City, Mexico
  • fYear
    2011
  • fDate
    July 31 2011-Aug. 5 2011
  • Firstpage
    2725
  • Lastpage
    2732
  • Abstract
    The adaptation made for the Polynomial Artificial Neural Networks (PANN) using not only integer exponentials but also fractional exponentials, have shown evidence of its better performance, especially, when it works with non-linear and chaotic time series. In this paper we show the comparison of the PANN improved model of fractional exponentials with a new logarithmic model. We show that this new model have even better performance than the last PANN improved model.
  • Keywords
    forecasting theory; neural nets; time series; chaotic time series; fractional exponentials; integer exponentials; logarithmic model; nonlinear time series; polynomial artificial neural networks; time series forecasting; Artificial neural networks; Genetic algorithms; Mathematical model; Modeling; Polynomials; Time series analysis; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2011 International Joint Conference on
  • Conference_Location
    San Jose, CA
  • ISSN
    2161-4393
  • Print_ISBN
    978-1-4244-9635-8
  • Type

    conf

  • DOI
    10.1109/IJCNN.2011.6033576
  • Filename
    6033576
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3499669