Zenith Tropospheric Delay Model in European Region Based on GPT3 and PSO-BPNN
Abstract We optimize the back propagation neural network(BPNN) by using particle swarm optimization(PSO), and establish an optimized GPT3 model(MGPT3). We use the zenith tropospheric delay(ZTD) data of 30 IGS stations in Europe for 366 days in 2020 as an example to compare the accuracy of MGPT3, UNB3m and GPT3 models in predicting ZTD. The results show that the RMSE of MGPT3 model is 18.49 mm, which improves the accuracy by 55.0% and 47.7% compared to UNB3m and GPT3 models, respectively.
Key words ：
ZTD modeling
particle swarm optimization
BP neural network
European region
accuracy analysis
Cite this article:
LI Jinyu,YU Xuexiang,WEI Min et al. Zenith Tropospheric Delay Model in European Region Based on GPT3 and PSO-BPNN[J]. jgg, 2024, 44(7): 693-697.
LI Jinyu,YU Xuexiang,WEI Min et al. Zenith Tropospheric Delay Model in European Region Based on GPT3 and PSO-BPNN[J]. jgg, 2024, 44(7): 693-697.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2024/V44/I7/693
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