Application of Three Neural Network Models in the Prediction ofStratospheric Wind Field
Wind field forecast is of great significance for aerostat trajectory prediction. Traditional theoretical models can only predict wind speed in the next few hours, while BP neural network models can predict wind speeds in next few days. Therefore, in this paper, BP neural network, genetic algorithm-B...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2019-01-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2019.05.003 |
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| Summary: | Wind field forecast is of great significance for aerostat trajectory prediction. Traditional theoretical models can only predict wind speed in the next few hours, while BP neural network models can predict wind speeds in next few days. Therefore, in this paper, BP neural network, genetic algorithm-BP neural network, particle swam optimization-BP neural network were introduced and used as the wind field prediction model. The input variables of neural network predictive model are historical wind field data, and the model outputs the future wind speed data of stratospheric bottom. Meanwhile, the prediction accuracy of the three neural networks is also compared. The simulation results show that the neural network model can be applied to the prediction of stratospheric wind field and the BP neural networks optimized by genetic algorithm and particle swarm optimization can greatly improve the prediction accuracy of BP neural network, and the forecast wind speed is following the true value in a certain period of time. The prediction accuracy of the BP neural network optimized by particle swarm optimization is slightly better than that of the BP neural network optimized by genetic algorithm. |
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| ISSN: | 2096-5427 |