| 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 Publication | 2017 IEEE Visual Communications and Image Processing, VCIP 2017 |
| 2018 | |
| Pages | 1-4 |
| Language | 英语 |
| ISBN | 9781538604625 |
| Abstract | The 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. |
| Keyword | Ionosphere Total Electron Content Deep Learning Bidirectional Long Short-term Memory Forecast |
| Conference Name | 2017 IEEE Visual Communications and Image Processing, VCIP 2017 |
| Conference Date | December 10, 2017 - December 13, 2017 |
| Conference Place | St. Petersburg, FL, United states |
| Indexed By | EI |
| Document Type | 会议论文 |
| Identifier | http://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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| File Name/Size | DocType | Version | Access | License | ||
| 201808305097.pdf（419KB） | 会议论文 | 开放获取 | CC BY-NC-SA | View Download | ||
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