Predictions of the maximum amplitude for solar cycle 23 and its subsequent behavior using nonlinear methods

Two nonlinear methods are employed for the prediction of the maximum amplitude for solar cycle 23 and its declining behavior. First, a new heuristic method based on the second derivative of the (conveniently smoothed) sunspot data is proposed. The curvature of the smoothed sunspot data at cycle mini...

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Published inSolar physics Vol. 191; no. 2; pp. 419 - 425
Main Authors Verdes, P F, Parodi, MA, Granitto, P M, Navone, H D, Piacentini, R D, Ceccatto, HA
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Nature B.V 01.02.2000
Subjects
Neural networks
Solar physics
Sunspots
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Summary:Two nonlinear methods are employed for the prediction of the maximum amplitude for solar cycle 23 and its declining behavior. First, a new heuristic method based on the second derivative of the (conveniently smoothed) sunspot data is proposed. The curvature of the smoothed sunspot data at cycle minimum appears to correlate (R 0.92) with the cycle's later-occurring maximum amplitude. Secondly, in order to predict the near-maximum and declining activity of solar cycle 23, a neural network analysis of the annual mean sunspot time series is also performed. The results of the present study are then compared with some other recent predictions.[PUBLICATION ABSTRACT]
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ISSN:0038-0938
1573-093X
DOI:10.1023/A:1005202814071