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| Titel |
Neural network prediction of geomagnetic activity: a method using local Hölder exponents |
| VerfasserIn |
Z. Vörös, D. Jankovičová |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 9, no. 5/6 ; Nr. 9, no. 5/6, S.425-433 |
| Datensatznummer |
250006557
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| Publikation (Nr.) |
 copernicus.org/npg-9-425-2002.pdf |
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| Zusammenfassung |
| 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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