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Solar flare forecasting using learning vector quantity and unsupervised clustering techniques
Li Rong1; Wang HuaNing2; Cui YanMei3; Huang Xin2
2011-08-01
发表期刊SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY
卷号54期号:8页码:1546-1552
摘要In this paper, a combined method of unsupervised clustering and learning vector quantity (LVQ) is presented to forecast the occurrence of solar flare. Three magnetic parameters including the maximum horizontal gradient, the length of the neutral line, and the number of singular points are extracted from SOHO/MDI longitudinal magnetograms as measures. Based on these parameters, the sliding-window method is used to form the sequential data by adding three days evolutionary information. Considering the imbalanced problem in dataset, the K-means clustering, as an unsupervised clustering algorithm, is used to convert imbalanced data to balanced ones. Finally, the learning vector quantity is employed to predict the flares level within 48 hours. Experimental results indicate that the performance of the proposed flare forecasting model with sequential data is improved.
关键词Photospheric Magnetic Field Sliding-windows Unsupervised Clustering Learning Vector Quantity (Lvq)
DOI10.1007/s11433-011-4391-0
收录类别SCI
语种英语
WOS记录号WOS:000292928900029
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文献类型期刊论文
条目标识符http://ir.bao.ac.cn/handle/114a11/8381
专题太阳物理研究部
作者单位1.Beijing WuZi Univ, Sch Informat, Beijing 101149, Peoples R China
2.Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China
3.Chinese Acad Sci, Ctr Space Sci & Appl Res, Beijing 100190, Peoples R China
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GB/T 7714
Li Rong,Wang HuaNing,Cui YanMei,et al. Solar flare forecasting using learning vector quantity and unsupervised clustering techniques[J]. SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY,2011,54(8):1546-1552.
APA Li Rong,Wang HuaNing,Cui YanMei,&Huang Xin.(2011).Solar flare forecasting using learning vector quantity and unsupervised clustering techniques.SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY,54(8),1546-1552.
MLA Li Rong,et al."Solar flare forecasting using learning vector quantity and unsupervised clustering techniques".SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY 54.8(2011):1546-1552.
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