东南大学学报（英文版）2022，Vol.38Issue(1)：20-26,7.DOI:10.3969/j.issn.1003-7985.2022.01.004
基于改进的多因素SVM算法的太阳射电暴强度检测
Detection of solar radio burst intensity based on a modified multifactor SVM algorithm
摘要
Abstract
To realize the automatic detection of solar radio burst(SRB)intensity,detection based on a modified multifactor support vector machine(SVM)algorithm is proposed.First,the influence of SRB on global navigation satellite system(GNSS)signals is analyzed.Feature vectors,which can reflect the SRB intensity of stations,are also extracted.SRB intensity is classified according to the solar radio flux,and different class labels correspond to different SRB intensity types.The training samples are composed of feature vectors and their corresponding class labels.Second,training samples are input into SVM classifiers to one-against-one training to obtain the optimal classification models.Finally,the optimal classification model is synthesized into a modified multifactor SVM classifier,which is used to automatically detect the SRB intensity of new data.Experimental results indicate that for historical SRB events,the average accuracy of SRB intensity detection is greater than 90％when the solar incident angle is higher than 20°.Compared with other methods,the proposed method considers many factors with higher accuracy and does not rely on radio telescopes,thereby saving cost.关键词
全球导航卫星系统/太阳射电爆发/改进的多因素支持向量机/检测精度Key words
global navigation satellite system/solar radio burst/modified multifactor SVM algorithm/detection accura-cy分类
信息技术与安全科学引用本文复制引用
罗铱镅,祝雪芬,林梦颖,杨帆,涂刚毅..基于改进的多因素SVM算法的太阳射电暴强度检测[J].东南大学学报（英文版）,2022,38(1):20-26,7.基金项目
The National Key Research and Development Plan of China(No.2018YFB0505103),the National Natural Science Founda-tion of China(No.61873064). （No.2018YFB0505103）
