Solar flare forecasting using learning vector quantity and unsupervised clustering techniques  

作　　者：LI Rong WANG HuaNing CUI YanMei HUANG Xin 

机构地区：[1]School of Information, Beijing WuZi University, Beijing 101149, China [2]The National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China [3]Center for Space Science and Applied Research, Chinese Academy of Sciences, Beijing 100190, China

出　　处：《Science China(Physics,Mechanics & Astronomy)》2011年第8期1546-1552,共7页中国科学：物理学、力学、天文学（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No. 10973020);the Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality (Grant No. PHR200906210);the Funding Project for Base Construction of Scientific Research of Beijing Municipal Commission of Education (Grant No. WYJD200902);Beijing Philosophy and Social Science Planning Project (Grant No. 09BaJG258)

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 pa- rameters, the sliding-window method is used to form the sequential data by adding three days evolutionary information. Con- sidering 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 pa- rameters, 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） 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] P182.52[自动化与计算机技术—计算机科学与技术]