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Exploiting solar wind time series correlation with magnetospheric response by using an hybrid neuro-wavelet approach

Published online by Cambridge University Press:  08 June 2011

Christian Napoli
Affiliation:
Dept. of Physics and Astronomy, University of Catania, Via S. Sofia, 95125, Catania - ITALY email: chnapoli@gmail.com
Francesco Bonanno
Affiliation:
Dept. of Electrical, Electronic and Systems Engineering, University of Catania, Viale A. Doria, 95125, Catania - ITALY email: gcapizzi@diees.unict.it
Giacomo Capizzi
Affiliation:
Dept. of Electrical, Electronic and Systems Engineering, University of Catania, Viale A. Doria, 95125, Catania - ITALY email: gcapizzi@diees.unict.it
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Abstract

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The studies about the Sun rise a strong interest regarding modifications caused by the solar activity on the Earth. For almost a century in literature was discussed the problem of forecasting and analysis of the space weather, which in his definition covers both the near-earth space and the biospheric affection due to the environmental interaction with the Sun. In particular in the last years increased the attention for magnetospheric response in conjunction with the technological infrastructure and the biosphere itself. This to prevent i.e. spacecraft failures or possible treats to human health. Since the main effect of the activity of the Sun is the solar wind, rises the aim to found a correlation between itself and the localized variations induced on the magnetosphere being the purpose to predict long-term variation of the magnetic field from solar wind time series. As recently proposed for solar wind forecasting, an hybrid approach will be here used than joining the wavelet analysis with the prediction capabilities of recurrent neural networks with an adaptive amplitude activation function algorithm in order to avoid the need to standardize or rescaling the input signal and to match the exact range of the activation function.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2011

References

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