结合主成分分析和支持向量机的太阳耀斑预报模型  

Solar flare forecasting model of combining principal component analysis with support vector machine

作　　者：袁飞[1,2] 林佳本[1] 邓元勇[1] 郭晶晶[1] 王刚[3] 曾真[1] 王丙祥[1] 

机构地区：[1]中国科学院太阳活动重点实验室,北京100012 [2]中国科学院大学,北京100049 [3]北京交通大学,北京100044

出　　处：《科学通报》2016年第20期2316-2321,共6页Chinese Science Bulletin

基　　金：国家自然科学基金(11373042;11373044;11427901);中国科学院知识创新工程项目(KJCX2-EW-T07)资助

摘　　要：太阳耀斑是剧烈的太阳活动现象之一,耀斑的预报对人类活动有着重要的实用价值.为进一步提高太阳耀斑的预报准确率,本文在综合考虑太阳黑子活动区参量、10.7 cm太阳射电流量等预报因子的前提下,提出了结合主成分分析和支持向量机的太阳耀斑预报模型.本模型的太阳黑子活动区参量包括黑子群面积、黑子群的Mc Intosh分类、活动区日面经度延伸、可见黑子数和黑子群的磁分类.本文首先对上述参量进行了合适的属性编码并归一化建模所需数据集,然后利用主成分分析方法提取出主要特征,应用支持向量机方法建立了耀斑预报模型.最后,本文将该模型预报结果与其他预报模型的结果进行了对比,结果验证了结合主成分分析和支持向量机的太阳耀斑预报模型是一种有效的预报模型.Solar flare is an important event in the solar atmosphere. It is a main disturbance resource of space environment and has a dramatic impact on human activities. How to promote the forecast ability of solar flare has a great significance for human beings. To better predict solar flare, this paper used principal component analysis and support vector machine, which are two classic machine learning methods, to predict solar flare. In feature extraction part, principal component analysis is a generally adopted method, and it is a technique for linearly compressing multidimensional data into lower dimensions with minimal loss of information. In flare classification part, support vector machine is a learning machine based on the statistical learning theory. The support vector machine can deal with nonlinear problems in classification and regression easily by using kernel functions, which is necessary to map onto another high dimension linear space. In solar flare forecasting research, the relationship between solar flare and morphological evolution of sunspots plays an important role in daily flare forecasting. And there is evidence that sunspot parameters and 10.7 cm solar radio flux are extremely related with solar flare. With the predictors fully taken into account, a new method of combing principal component analysis with support vector machine is proposed to improve the forecast ability of solar flare. In this paper, the sunspot parameters are area of sunspot group, Mc Intosh classification, extended longitude, the sunspot number in the solar active region and magnetic classification. Using attribute coding and appropriate transform function, the initial data set of predictors is normalized to form the modeling data set. And based on this data set, principal component analysis and support vector machine are applied to build solar flare forecasting model. In experiment, the forecasting model is compared with other model, which works well in the solar flare short-term forecasting. This shows that the PCA-SVM predicti

关 键 词：太阳耀斑 预报因子 支持向量机 主成分分析 黑子群 

分 类 号：P182.52[天文地球—天文学]