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Solar flare forecasting using learning vector quantity and unsupervised clustering techniques
Li Rong; Wang HuaNing; Cui YanMei; Huang Xin; Li, R (reprint author), Beijing WuZi Univ, Sch Informat, Beijing 101149, Peoples R China.
Department空间环境部
Source PublicationSCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY
2011
Volume54Issue:8Pages:1546-1552
ISSN1674-7348
Language英语
KeywordPhotospheric Magnetic Field Sliding-windows Unsupervised Clustering Learning Vector Quantity (Lvq)
AbstractIn 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.; 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.
Indexed BySCI ; EI
Funding Project中国科学院空间科学与应用研究中心
Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/2921
Collection空间环境部
Corresponding AuthorLi, R (reprint author), Beijing WuZi Univ, Sch Informat, Beijing 101149, Peoples R China.
Recommended Citation
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,&Li, R .(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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