FORECASTING OF IONOSPHERIC VERTICAL TOTAL ELECTRON CONTENT (TEC) USING LSTM NETWORKS
文献类型：会议论文
| 作者 | Sun, Wenqing; Xu, Long; Huang, Xin; Zhang, Weiqiang; Yuan, Tianjiao; Chen, Zhuo; Yan, Yihua; 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. |
| 出版日期 | 2017 |
| 会议日期 | JUL 09-12, 2017 |
| 会议地点 | Ningbo, PEOPLES R CHINA |
| 关键词 | Ionospheric Tec Lstm Forecast |
| 页码 | 340-344 |
| 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. |
| 会议录 | PROCEEDINGS OF 2017 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 2
 |
| 语种 | 英语 |
| ISSN号 | 2160-133X |
| ISBN号 | 978-1-5386-0408-3 |
| 源URL | [http://ir.nssc.ac.cn/handle/122/6230]  |
| 专题 | 国家空间科学中心_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. |
| 推荐引用方式 GB/T 7714 | Sun, Wenqing,Xu, Long,Huang, Xin,et al. FORECASTING OF IONOSPHERIC VERTICAL TOTAL ELECTRON CONTENT (TEC) USING LSTM NETWORKS[C]. 见:. Ningbo, PEOPLES R CHINA. JUL 09-12, 2017. |
入库方式： OAI收割
来源：国家空间科学中心
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