DocumentCode :
2841225
Title :
Prediction of the Smoothed Monthly Mean Sunspot Area Using Artificial Neural Metwork
Author :
Ding, Liuguan ; Jiang, Yong ; Lan, Rushi
fYear :
2012
fDate :
24-25 July 2012
Firstpage :
33
Lastpage :
36
Abstract :
Sunspot area is an important feature to measure the solar activities. Prediction of sunspot area can provide useful information for solar activities and space weather studies etc. In this paper, we propose a smoothed monthly mean sunspot area prediction method using artificial neural network. The prediction model is built by training the area data before the eighteenth solar cycle, and then forecast the data after the eighteenth solar week. We also consider the influence of different training step and prediction step respectively. Experimental results demonstrate the effectiveness of the proposed method. Finally, we forecast the smoothed monthly mean sunspot area from March 2011 to March 2012.
Keywords :
Artificial neural networks; Data models; Educational institutions; Magnetic flux; Predictive models; Training; artificial neural network; prediction; solar acitve; solar sunspot area;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Computing Science (ICIC), 2012 Fifth International Conference on
Conference_Location :
Liverpool, United Kingdom
ISSN :
2160-7443
Print_ISBN :
978-1-4673-1985-0
Type :
conf
DOI :
10.1109/ICIC.2012.42
Filename :
6258064
Link To Document :
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