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Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015 pp 465–475Cite as

Prediction of NOX Concentration Time Series Using the Chaos Theory

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 427))

Abstract

This paper is aimed at analysis of NOx concentration time series. At first we estimated the time delay and the embedding dimension, which is needed for the Lyapunov exponent estimation and for the phase space reconstruction. Subsequently we computed the largest Lyapunov exponent, which is one of the important indicators of chaos. Then we estimated the correlation dimension and Kolmogorov entropy. The results indicated that chaotic behaviors obviously exist in NOx concentration time series. Finally we computed predictions using a radial basis function and polynomials to fit global nonlinear functions to the data.

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Acknowledgments

This work was supported by SGS 40520/20SG/450009 UPCE.

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Authors and Affiliations

  1. University of Pardubice, Pardubice, Czech Republic

    Radko Kříž & Petra Lešáková

Authors
  1. Radko Kříž
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  2. Petra Lešáková
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Corresponding author

Correspondence to Radko Kříž .

Editor information

Editors and Affiliations

  1. Sci. Network for Innovation & Rec. Exell, Machine Intelligence Research Labs (MIR, Auburn, Washington, USA

    Ajith Abraham

  2. Membre du Groupe Efrei, ESIGETEL, Villejuif, France

    Katarzyna Wegrzyn-Wolska

  3. Faculty of Computers & Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  4. VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Vaclav Snasel

  5. ENIS, University of Sfax, Sfax, Tunisia

    Adel M. Alimi

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© 2016 Springer International Publishing Switzerland

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Kříž, R., Lešáková, P. (2016). Prediction of NOX Concentration Time Series Using the Chaos Theory. In: Abraham, A., Wegrzyn-Wolska, K., Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015. Advances in Intelligent Systems and Computing, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-29504-6_44

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  • DOI: https://doi.org/10.1007/978-3-319-29504-6_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29503-9

  • Online ISBN: 978-3-319-29504-6

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