| 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 Publication | SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY
 |
| 2011 | |
| Volume | 54Issue:8Pages:1546-1552 |
| ISSN | 1674-7348 |
| Language | 英语 |
| Keyword | Photospheric Magnetic Field Sliding-windows Unsupervised Clustering Learning Vector Quantity (Lvq) |
| Abstract | 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.; 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 By | SCI ; 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 |
| Identifier | http://ir.nssc.ac.cn/handle/122/2921 |
| Collection | 空间环境部 |
| Corresponding Author | Li, 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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| 20115481546.pdf（533KB） | 开放获取 | CC BY-NC-SA | View Download | |||
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