Research on ionospheric parameters prediction based on deep learning
For ionospheric parameter prediction, the short-term and daily mean value prediction of ionospheric parameters was established by long short-term memory (LSTM) predictive neural network modeling.Two methods of point-by-point prediction and sequence prediction were utilized.Furthermore, in order to p...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2021-04-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021097/ |
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| _version_ | 1841539288751144960 |
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| author | Yuntian FENG Xia WU Xiong XU Rongqing ZHANG |
| author_facet | Yuntian FENG Xia WU Xiong XU Rongqing ZHANG |
| author_sort | Yuntian FENG |
| collection | DOAJ |
| description | For ionospheric parameter prediction, the short-term and daily mean value prediction of ionospheric parameters was established by long short-term memory (LSTM) predictive neural network modeling.Two methods of point-by-point prediction and sequence prediction were utilized.Furthermore, in order to predict the hourly and daily changes of ionospheric parameters, the proposed scheme was optimized by multidimensional prediction and empirical mode decomposition (EMD) algorithm.Finally, the feasibility of the proposed optimization algorithm in improving the prediction accuracy of ionospheric parameters is verified. |
| format | Article |
| id | doaj-art-8af05db8cc6d4cba95597f886dd26649 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2021-04-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-8af05db8cc6d4cba95597f886dd266492025-01-14T07:22:03ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-04-014220220659741706Research on ionospheric parameters prediction based on deep learningYuntian FENGXia WUXiong XURongqing ZHANGFor ionospheric parameter prediction, the short-term and daily mean value prediction of ionospheric parameters was established by long short-term memory (LSTM) predictive neural network modeling.Two methods of point-by-point prediction and sequence prediction were utilized.Furthermore, in order to predict the hourly and daily changes of ionospheric parameters, the proposed scheme was optimized by multidimensional prediction and empirical mode decomposition (EMD) algorithm.Finally, the feasibility of the proposed optimization algorithm in improving the prediction accuracy of ionospheric parameters is verified.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021097/LSTMionospheremultidimensional predictionEMD |
| spellingShingle | Yuntian FENG Xia WU Xiong XU Rongqing ZHANG Research on ionospheric parameters prediction based on deep learning Tongxin xuebao LSTM ionosphere multidimensional prediction EMD |
| title | Research on ionospheric parameters prediction based on deep learning |
| title_full | Research on ionospheric parameters prediction based on deep learning |
| title_fullStr | Research on ionospheric parameters prediction based on deep learning |
| title_full_unstemmed | Research on ionospheric parameters prediction based on deep learning |
| title_short | Research on ionospheric parameters prediction based on deep learning |
| title_sort | research on ionospheric parameters prediction based on deep learning |
| topic | LSTM ionosphere multidimensional prediction EMD |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021097/ |
| work_keys_str_mv | AT yuntianfeng researchonionosphericparameterspredictionbasedondeeplearning AT xiawu researchonionosphericparameterspredictionbasedondeeplearning AT xiongxu researchonionosphericparameterspredictionbasedondeeplearning AT rongqingzhang researchonionosphericparameterspredictionbasedondeeplearning |