| Citation: | LONG Yuan, DENG Xiaolong, YANG Xixiang, et al. Short-term rapid prediction of stratospheric wind field based on PSO-BP neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(10): 1970-1978. doi: 10.13700/j.bh.1001-5965.2021.0068(in Chinese)
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The stratospheric wind field environment has an important influence on the flight performance of near space low speed aircraft. In this paper, the modeling and prediction methods of stratospheric regional wind field are investigated based on PSO-BP neural network. Firstly, the principal component analysis method is implemented to reduce the dimensions of the historical wind field data. Then, the BP neural network, which is trained by the processed data to predict the wind field, is optimized with particle swarm optimization (PSO) algorithm. Finally, the Biharmonic spline surface interpolation method using multi-point prediction wind fields is studied to construct the regional prediction wind field. Taking the 5-year historical wind field data of a certain place, comparative study of the wind field prediction model based on BP neural network and PSO-BP neural network is conducted. The results show that PSO algorithm, which is characterized global optimization, can improve BP neural network by avoiding the disadvantage of easily falling into local optimization, and enhance the prediction accuracy. The integration method of PSO-BP neural network prediction and Biharmonic spline surface interpolation can provide the prediction of the regional wind field. The proposed method can provide high precision regional prediction wind field for trajectory planning and station keeping of near space low speed aircraft.
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