Short-term prediction network for short-wave MUF based on model-data dual-driven
Predicting the maximum available frequency of short-wave communication presents the challenges of low prediction accuracy of classical prediction model methods and difficulty in obtaining training set data for machine learning prediction methods.To address this issue, a model-data dual-driven bidire...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2023-12-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023234/ |
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| _version_ | 1841540029590011904 |
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| author | Junbing LI Youjun ZENG Xiaoping ZENG Guojun LI Chenxi BAI |
| author_facet | Junbing LI Youjun ZENG Xiaoping ZENG Guojun LI Chenxi BAI |
| author_sort | Junbing LI |
| collection | DOAJ |
| description | Predicting the maximum available frequency of short-wave communication presents the challenges of low prediction accuracy of classical prediction model methods and difficulty in obtaining training set data for machine learning prediction methods.To address this issue, a model-data dual-driven bidirectional gated recurrent unit (BiGRU) network for short-term prediction of MUF was proposed.On the model-driven, a large-scale dataset generated by the classical MUF prediction model was used as the model-driven training set, and a preliminary network was obtained after joint learning of the 2D CNN and the BiGRU network.On the data-driven, the preliminary network was trained twice using a small-scale measured dataset to obtain the final network CNN-BiGRU-NN.The simulation results show that the proposed network has reduced average root mean squared error (RMSE) at both daily and momentary scales compared with the GRU network, LSTM network and VOACAP model. |
| format | Article |
| id | doaj-art-30fe2aa177c74beaaca45d13717de9f0 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-12-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-30fe2aa177c74beaaca45d13717de9f02025-01-14T06:22:27ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-12-01449911159384533Short-term prediction network for short-wave MUF based on model-data dual-drivenJunbing LIYoujun ZENGXiaoping ZENGGuojun LIChenxi BAIPredicting the maximum available frequency of short-wave communication presents the challenges of low prediction accuracy of classical prediction model methods and difficulty in obtaining training set data for machine learning prediction methods.To address this issue, a model-data dual-driven bidirectional gated recurrent unit (BiGRU) network for short-term prediction of MUF was proposed.On the model-driven, a large-scale dataset generated by the classical MUF prediction model was used as the model-driven training set, and a preliminary network was obtained after joint learning of the 2D CNN and the BiGRU network.On the data-driven, the preliminary network was trained twice using a small-scale measured dataset to obtain the final network CNN-BiGRU-NN.The simulation results show that the proposed network has reduced average root mean squared error (RMSE) at both daily and momentary scales compared with the GRU network, LSTM network and VOACAP model.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023234/short-wave communicationmaximum usable frequencyshort-term predictionmodel-data dual-drivenCNN-BiGRU-NN |
| spellingShingle | Junbing LI Youjun ZENG Xiaoping ZENG Guojun LI Chenxi BAI Short-term prediction network for short-wave MUF based on model-data dual-driven Tongxin xuebao short-wave communication maximum usable frequency short-term prediction model-data dual-driven CNN-BiGRU-NN |
| title | Short-term prediction network for short-wave MUF based on model-data dual-driven |
| title_full | Short-term prediction network for short-wave MUF based on model-data dual-driven |
| title_fullStr | Short-term prediction network for short-wave MUF based on model-data dual-driven |
| title_full_unstemmed | Short-term prediction network for short-wave MUF based on model-data dual-driven |
| title_short | Short-term prediction network for short-wave MUF based on model-data dual-driven |
| title_sort | short term prediction network for short wave muf based on model data dual driven |
| topic | short-wave communication maximum usable frequency short-term prediction model-data dual-driven CNN-BiGRU-NN |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023234/ |
| work_keys_str_mv | AT junbingli shorttermpredictionnetworkforshortwavemufbasedonmodeldatadualdriven AT youjunzeng shorttermpredictionnetworkforshortwavemufbasedonmodeldatadualdriven AT xiaopingzeng shorttermpredictionnetworkforshortwavemufbasedonmodeldatadualdriven AT guojunli shorttermpredictionnetworkforshortwavemufbasedonmodeldatadualdriven AT chenxibai shorttermpredictionnetworkforshortwavemufbasedonmodeldatadualdriven |