On the application of information theory to the optimum state-space reconstruction of the short-term solar radio flux (10.7cm), and its prediction via a neural network

Details On the application of information theory to the optimum state-space reconstruction of the short-term solar radio flux (10.7cm), and its prediction via a neural network On the application of information theory to the optimum state-space reconstruction of the short-term solar radio flux (10.7cm), and its prediction via a neural network

May 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001mnras.323..101c&link_type=abstract

Monthly Notices of the Royal Astronomical Society, Volume 323, Issue 1, pp. 101-108.

Astronomy and Astrophysics

Astronomy

2

Methods: Data Analysis, Sun: Atmosphere, Sun: Radio Radiation

Scientific paper

The prediction of a time series using a neural network involves an optimum state-space reconstruction. The state space of the daily 10.7-cm solar radio flux is reconstructed using an information theory approach. A multi-layer feed-forward neural net is used for short-term prediction of the time series. The convergence of the synaptic weights is obtained partially by simulated annealing and partially by the `quick prop' variation of back-propagation. The result gives a reasonably accurate short-term prediction.

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