西安电子科技大学学报（自然科学版）Issue(5)：147-153,206,8.DOI:10.3969/j.issn.1001-2400.2015.05.025
支持向量机用于电离层 foF2的短期区域预报
On the short-term regional prediction of foF2 based on the support vector machine
摘要
Abstract
Ionospheric short‐term forecasting is very important to radio communication , navigation and radar systems . In this paper , in order to improve the regional prediction accuracy of ionosphere , a model of regional prediction of the ionospheric F2 layer critical frequency in China area 1 hour in advance is set up based on the support vector machine (Support Vector Machine , referred to as SVM for short) method . In this model , the influence of solar activity , geomagnetic activity , the upper atmosphere , geographical location and other factors on the ionosphere is taken into consideration . Results of this model is compared to Back‐Propagation referred to as BP for short the neural network of the same input parameters and the IRI model ( International Reference Ionosphere , referred to as IRI for short) . The results show that the average relative error of annual prediction of SVM in high solar activity years decreases by 2.5% and 9.6% , respectively , compared with the neural network and the IRI models and in low solar activity decreases by 1.8% and 7.5% , respectively . In the low latitude area , the prediction of SVM has more significant advantages over the BP neural network . In the high and low solar activity years it decreases by 3.2% and 2.7% , respectively . During the storm time SVM also shows a relatively good prediction ability . This proves that the developed model based on SVM in the paper has more advantages over the BP neural network and IRI model .关键词
支持向量机/电离层f o F2/区域预报/对比分析Key words
support vector machine/ionospheric foF2/regional prediction/comparative analysis分类
天文与地球科学引用本文复制引用
李美玲,胡耀垓,周晨,赵正予,张援农,刘静,邓忠新..支持向量机用于电离层 foF2的短期区域预报[J].西安电子科技大学学报（自然科学版）,2015,(5):147-153,206,8.基金项目
国家自然科学基金资助项目（41327002，41375007）；湖北省自然科学基金青年杰出人才资助项目 （）
