Solar activity forecast: Spectral analysis and neurofuzzy prediction

被引:56
作者
Gholipour, A [1 ]
Lucas, C
Araabi, BN
Shafiee, M
机构
[1] Univ Tehran, Control & Intelligent Proc Ctr Excellence, Dept Elect & Comp Engn, Tehran, Iran
[2] Sch Cognit Sci, Inst Studies Theoret Phys & Math, IPM, Tehran, Iran
[3] Amirkabir Univ Technol, Fac Elect Engn, Tehran, Iran
关键词
solar activity; sunspot numbers; forecasting; singular spectrum analysis; neurofuzzy modeling; long-term prediction;
D O I
10.1016/j.jastp.2004.12.001
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 [地球物理学]; 070902 [地球化学];
摘要
Active research in the last two decades indicates that the physical precursor and solar dynamo techniques are preferred as practical tools for long-term prediction of solar activity. But why should we omit more than 23 cycles of solar activity history, and just use empirical methods or simple autoregressive methods on the basis of observations for the latest eight cycles? In this article, a method based on spectral analysis and neurofuzzy modeling is proposed that is capable of issuing very accurate long-term prediction of sunspot number time series. A locally linear neurofuzzy model is optimized for each of the principal components obtained from singular spectrum analysis, and the multi-step predicted values are recombined to make the sunspot number time series. The proposed method is used for solar cycles 22 and 23 and the results are remarkably good in comparison to the predictions made by solar dynamo and precursor methods. An early prediction of the maximum smoothed international sunspot number for cycle 24 is 145 in 2011-2012. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码:595 / 603
页数:9
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