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International Conference on Artificial Neural Networks

ICANN 2005: Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 pp 533–539Cite as

Counterpropagation with Delays with Applications in Time Series Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3697))

Abstract

The paper presents a method for time series prediction using a complete counterpropagation network with delay kernels. Our network takes advantage of the clustering and mapping capability of the original CPN combined with dynamical elements and become able to discover and approximate the strongest topological and temporal relationships among the fields in the data. Experimental results using two chaotic time series and a set of astrophysical data validate the performance of the proposed method.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .

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Author information

Authors and Affiliations

  1. Institute of Scientific Computing, University of Salzburg, Austria

    Carmen Fierascu

Authors
  1. Carmen Fierascu
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Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

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© 2005 Springer-Verlag Berlin Heidelberg

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Fierascu, C. (2005). Counterpropagation with Delays with Applications in Time Series Prediction. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_84

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  • DOI: https://doi.org/10.1007/11550907_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28755-1

  • Online ISBN: 978-3-540-28756-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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