A Short-Term Forecast Model of foF2 Based on Elman Neural Network
The critical frequency foF2 of the ionosphere F2 layer is one of the most important parameters of the ionosphere. Based on the Elman neural network (ENN), this paper constructs a single station forecasting model to predict foF2 one hour ahead. In order to avoid the network falling into local minimum...
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| Format: | Article |
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MDPI AG
2019-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/14/2782 |
| _version_ | 1818274098872057856 |
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| author | Jieqing Fan Chao Liu Yajing Lv Jing Han Jian Wang |
| author_facet | Jieqing Fan Chao Liu Yajing Lv Jing Han Jian Wang |
| author_sort | Jieqing Fan |
| collection | DOAJ |
| description | The critical frequency foF2 of the ionosphere F2 layer is one of the most important parameters of the ionosphere. Based on the Elman neural network (ENN), this paper constructs a single station forecasting model to predict foF2 one hour ahead. In order to avoid the network falling into local minimum, the model is optimized by the improved particle swarm optimization (IPSO). The input parameters used in the model include local time, seasonal information, solar cycle information and magnetic activity information. Data of the Wuhan Station from 2008 to 2016 were used to train and test the model. The prediction results of foF2 show that the root mean square error (RMSE) of the Elman neural network model is 4.30% lower than that of the back-propagation neural network (BPNN) model. The RMSE is further reduced by 8.92% after using the IPSO to optimize the model. This indicates that the Elman neural network model optimized by the improved particle swarm optimization is superior to the BP neural network and Elman neural network in the forecast of foF2 one hour ahead at Wuhan station. |
| first_indexed | 2024-12-12T22:08:28Z |
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| id | doaj.art-1545a31102204cd4bdb9a8aaa724c8de |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-12T22:08:28Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1545a31102204cd4bdb9a8aaa724c8de2022-12-22T00:10:18ZengMDPI AGApplied Sciences2076-34172019-07-01914278210.3390/app9142782app9142782A Short-Term Forecast Model of foF2 Based on Elman Neural NetworkJieqing Fan0Chao Liu1Yajing Lv2Jing Han3Jian Wang4School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaSchool of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaSchool of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaChina Research Institute of Radiowave Propagation, Qingdao 266107, ChinaChina Research Institute of Radiowave Propagation, Qingdao 266107, ChinaThe critical frequency foF2 of the ionosphere F2 layer is one of the most important parameters of the ionosphere. Based on the Elman neural network (ENN), this paper constructs a single station forecasting model to predict foF2 one hour ahead. In order to avoid the network falling into local minimum, the model is optimized by the improved particle swarm optimization (IPSO). The input parameters used in the model include local time, seasonal information, solar cycle information and magnetic activity information. Data of the Wuhan Station from 2008 to 2016 were used to train and test the model. The prediction results of foF2 show that the root mean square error (RMSE) of the Elman neural network model is 4.30% lower than that of the back-propagation neural network (BPNN) model. The RMSE is further reduced by 8.92% after using the IPSO to optimize the model. This indicates that the Elman neural network model optimized by the improved particle swarm optimization is superior to the BP neural network and Elman neural network in the forecast of foF2 one hour ahead at Wuhan station.https://www.mdpi.com/2076-3417/9/14/2782foF2Elman neural networkimproved particle swarm optimizationforecast |
| spellingShingle | Jieqing Fan Chao Liu Yajing Lv Jing Han Jian Wang A Short-Term Forecast Model of foF2 Based on Elman Neural Network Applied Sciences foF2 Elman neural network improved particle swarm optimization forecast |
| title | A Short-Term Forecast Model of foF2 Based on Elman Neural Network |
| title_full | A Short-Term Forecast Model of foF2 Based on Elman Neural Network |
| title_fullStr | A Short-Term Forecast Model of foF2 Based on Elman Neural Network |
| title_full_unstemmed | A Short-Term Forecast Model of foF2 Based on Elman Neural Network |
| title_short | A Short-Term Forecast Model of foF2 Based on Elman Neural Network |
| title_sort | short term forecast model of fof2 based on elman neural network |
| topic | foF2 Elman neural network improved particle swarm optimization forecast |
| url | https://www.mdpi.com/2076-3417/9/14/2782 |
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