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题名: FORECASTING OF IONOSPHERIC VERTICAL TOTAL ELECTRON CONTENT (TEC) USING LSTM NETWORKS
作者: Sun, Wenqing; Xu, Long; Huang, Xin; Zhang, Weiqiang; Yuan, Tianjiao; Chen, Zhuo; Yan, Yihua
作者部门: 空间环境部
通讯作者: Sun, WQ (reprint author), Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing, Peoples R China. ; Sun, WQ (reprint author), Shenzhen Univ, Coll Math & Stat, Shenzhen, Peoples R China.
会议名称: International Conference on Machine Learning and Cybernetics (ICMLC)
会议日期: JUL 09-12, 2017
会议地点: Ningbo, PEOPLES R CHINA
会议录: PROCEEDINGS OF 2017 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 2
出版日期: 2017
收录类别: EI ; CPCI
页码: 340-344
ISSN号: 2160-133X
ISBN号: 978-1-5386-0408-3
关键词: Ionospheric ; TEC ; LSTM ; Forecast
英文摘要: Ionosphere is an important space environment near the earth. Its disturbance would result in severe propagation effects to radio information system, thus causing bad influences on communication, navigation, radar and so on. The total electron content (TEC) is an important parameter to present the disturbance of ionosphere, so TEC forecast is very meaningful in scientific research field. In this paper, we propose a long short-term memory (LSTM) based model to predict ionospheric vertical TEC of Beijing. The input of our model is a time sequence consisting of the vector of daily TECs and other closely related parameters. The output is TECs of future 24 hours. The result shows the root of mean square (RMS) error of test data can reach 3.50 and RMS error is less than this number during the period of low solar activity. Compared to multilayer perceptron network, LSTM is more promising and reliable to forecast ionospheric TEC.
语种: 英语
内容类型: 会议论文
URI标识: http://ir.nssc.ac.cn/handle/122/6230
Appears in Collections:空间环境部_会议论文

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