Prediction of Solar Activity Based on Neuro-Fuzzy Modeling

Details Prediction of Solar Activity Based on Neuro-Fuzzy Modeling Prediction of Solar Activity Based on Neuro-Fuzzy Modeling

Mar 2005

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005soph..227..177a&link_type=abstract

Solar Physics, Volume 227, Issue 1, pp.177-191

Physics

5

Scientific paper

This paper presents an application of the neuro-fuzzy modeling to analyze the time series of solar activity, as measured through the relative Wolf number. The neuro-fuzzy structure is optimized based on the linear adapted genetic algorithm with controlling population size (LAGA-POP). Initially, the dimension of the time series characteristic attractor is obtained based on the smallest regularity criterion (RC) and the neuro-fuzzy model. Then the performance of the proposed approach, in forecasting yearly sunspot numbers, is favorably compared to that of other published methods. Finally, a comparison predictions for the remaining part of the 22nd and the whole 23rd cycle of the solar activity are presented.

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