NSSC OpenIR  > 空间环境部
FORECASTING OF IONOSPHERIC VERTICAL TOTAL ELECTRON CONTENT (TEC) USING LSTM NETWORKS
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.
Department空间环境部
Source PublicationPROCEEDINGS OF 2017 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 2
2017
Pages340-344
Language英语
ISSN2160-133X
ISBN978-1-5386-0408-3
AbstractIonosphere 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.
KeywordIonospheric Tec Lstm Forecast
Conference NameInternational Conference on Machine Learning and Cybernetics (ICMLC)
Conference DateJUL 09-12, 2017
Conference PlaceNingbo, PEOPLES R CHINA
Indexed ByEI ; CPCI
Document Type会议论文
Identifierhttp://ir.nssc.ac.cn/handle/122/6230
Collection空间环境部
Corresponding AuthorSun, 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.
Recommended Citation
GB/T 7714
Sun, Wenqing,Xu, Long,Huang, Xin,et al. FORECASTING OF IONOSPHERIC VERTICAL TOTAL ELECTRON CONTENT (TEC) USING LSTM NETWORKS[C],2017:340-344.
Files in This Item:
File Name/Size DocType Version Access License
201708108945.pdf(505KB)会议论文 开放获取CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Sun, Wenqing]'s Articles
[Xu, Long]'s Articles
[Huang, Xin]'s Articles
Baidu academic
Similar articles in Baidu academic
[Sun, Wenqing]'s Articles
[Xu, Long]'s Articles
[Huang, Xin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Sun, Wenqing]'s Articles
[Xu, Long]'s Articles
[Huang, Xin]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.