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Solar flare forecasting using learning vector quantity and unsupervised clustering techniques

文献类型：期刊论文

作者Li Rong; Wang HuaNing; Cui YanMei; Huang Xin
刊名SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY
出版日期2011
卷号54期号:8页码:1546-1552
关键词photospheric magnetic field sliding-windows unsupervised clustering learning vector quantity (LVQ)
ISSN号1674-7348
通讯作者Li, R (reprint author), Beijing WuZi Univ, Sch Informat, Beijing 101149, Peoples R China.
中文摘要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.
英文摘要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.
学科主题空间环境
收录类别SCI ; EI
资助信息National Natural Science Foundation of China [10973020]; Jurisdiction of Beijing Municipality [PHR200906210]; Beijing Municipal Commission of Education [WYJD200902]; Beijing Philosophy and Social Science Planning Project [09BaJG258]
语种英语
公开日期2014-12-15
源URL[http://ir.nssc.ac.cn/handle/122/2921]  
专题国家空间科学中心_空间环境部
推荐引用方式
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.

入库方式： OAI收割

来源：国家空间科学中心

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