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Modeling and predicting sunspot activity‐state space reconstruction + artificial neural network methods
Author(s) -
Kulkarni D. R.,
Pandya A. S.,
Parikh J. C.
Publication year - 1998
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/98gl00136
Subject(s) - sunspot , artificial neural network , sunspot number , state space , nonparametric statistics , space weather , computer science , statistical physics , meteorology , artificial intelligence , solar cycle , mathematics , physics , statistics , solar wind , quantum mechanics , magnetic field
Ideas of state space reconstruction of dynamics are combined with nonparametric artificial neural network approach to model sunspot activity. The structural aspects of the model are for the most part determined from the sunspot data. The model gives a very good fit to the data. Further it predicts weaker solar activity in the current (23‐rd) cycle, with a maximum of 144±36.