摘要
利用粒子群优化算法(PSO)优化反向传播神经网络(BPNN),建立优化的GPT3模型(MGPT3)。以欧洲地区30个IGS测站2020年连续366 d的天顶对流层延迟(ZTD)数据为例进行实验,对比MGPT3、UNB3m和GPT3模型预测ZTD的精度。结果表明,MGPT3模型的RMSE为18.49 mm,相较于UNB3m和GPT3模型,其精度分别提高55.0%和47.7%。
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.
作者
李金羽
余学祥
魏民
刘金涛
LI Jinyu;YU Xuexiang;WEI Min;LIU Jintao(School of Spatial Information and Surveying Engineering,Anhui University of Science and Technology,Huainan 232001,China;Coal Industry Engineering Research Center of Ming Area Environmental and Disaster Cooperative Monitoring,Anhui University of Science and Technology,Huainan 232001,China;Key Laboratory of Aviation-Aerospace-Ground Cooperative Monitoring and Early Warning of Coal Mining-Induced Disasters of Anhui Higher Education Institutes,Anhui University of Science and Technology,Huainan 232001,China)
出处
《大地测量与地球动力学》
CSCD
北大核心
2024年第7期693-697,共5页
Journal of Geodesy and Geodynamics
基金
安徽省科技重大专项(202103a05020026)
安徽省重点研发计划(202104a07020014)。
关键词
ZTD建模
粒子群优化
BP神经网络
欧洲地区
精度分析
ZTD modeling
particle swarm optimization
BP neural network
European region
accuracy analysis
