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Bidirectional LSTM for ionospheric vertical Total Electron Content (TEC) forecasting
Sun, Wenqing; Xu, Long; Huang, Xin; Zhang, Weiqiang; Yuan, Tianjiao; Yan, Yihua
Department空间环境部
Source Publication2017 IEEE Visual Communications and Image Processing, VCIP 2017
2018
Pages1-4
Language英语
ISBN9781538604625
AbstractThe ionosphere is a region over earth's atmosphere which is ionized by solar radiation. It plays an important part in atmospheric electricity and forms the inner edge of the magnetosphere, furthermore, it has practical importance for its effects on radio propagation to earth. Accordingly, its cyclic changes and disturbances could influence communication, navigation, radar seriously. Total Electron Content (TEC) is an important parameter reflecting ionospheric significant characteristic. We can analyse ionospheric disturbance and cyclic change by investigating TEC. Now, the time sequence of TEC and relevant parameters is available for our research. In addition, Bidirectional long short-term memory (Bi-LSTM) is believed to have potential in time sequence processing. Hence, we investigate TEC forecast by referring to Bi-LSTM in this work. Experimental results demonstrate that Bi-LSTM can capture the cyclic change feature of TEC, and perform better than LSTM and multi-LSTM for TEC forecast. © 2017 IEEE.
KeywordIonosphere Total Electron Content Deep Learning Bidirectional Long Short-term Memory Forecast
Conference Name2017 IEEE Visual Communications and Image Processing, VCIP 2017
Conference DateDecember 10, 2017 - December 13, 2017
Conference PlaceSt. Petersburg, FL, United states
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.nssc.ac.cn/handle/122/6377
Collection空间环境部
Recommended Citation
GB/T 7714
Sun, Wenqing,Xu, Long,Huang, Xin,et al. Bidirectional LSTM for ionospheric vertical Total Electron Content (TEC) forecasting[C],2018:1-4.
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