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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 5/6
Nonlin. Processes Geophys., 9, 425–433, 2002
https://doi.org/10.5194/npg-9-425-2002
© Author(s) 2002. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Special issue: New Perspectives in Magnetospheric Dynamics: Chaos, Fractals,...

Nonlin. Processes Geophys., 9, 425–433, 2002
https://doi.org/10.5194/npg-9-425-2002
© Author(s) 2002. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  31 Dec 2002

31 Dec 2002

Neural network prediction of geomagnetic activity: a method using local Hölder exponents

Z. Vörös and D. Jankovičová Z. Vörös and D. Jankovičová
  • Geomagnetic Observatory Hurbanovo, Geophysical Institute SAS, Slovak Republic

Abstract. Local scaling and singularity properties of solar wind and geomagnetic time series were analysed using Hölder exponents . It was shown that in analysed cases due to the multifractality of fluctuations, α changes from point to point. We argued there exists a peculiar interplay between regularity/irregularity and amplitude characteristics of fluctuations which could be exploited for the improvement of predictions of geomagnetic activity. To this end, a layered back-propagation artificial neural network model with feedback connection was used for the study of the solar wind magnetosphere coupling and prediction of the geomagnetic Dst index. The solar wind input was taken from the principal component analysis of the interplanetary magnetic field, proton density and bulk velocity. Superior network performance was achieved in cases when the information on local Hölder exponents was added to the input layer.

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