| 基于神经网络模型误差补偿技术的对流层延迟模型研究 |
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| 引用本文： | 陈阳,胡伍生,严宇翔,龙凤阳,张良.基于神经网络模型误差补偿技术的对流层延迟模型研究[J].大地测量与地球动力学,2018,38(6):577-580. |
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| 作者姓名： | 陈阳 胡伍生 严宇翔 龙凤阳 张良 |
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| 作者单位： | 东南大学交通学院 |
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| 摘 要： | 针对传统对流层延迟模型精度较低的缺点，基于神经网络模型误差补偿技术，在Hopfield模型基础上建立一个适用于北半球的高精度融合模型。以Wyoming大学提供的2010年全球120多个观测台站的气象探空数据精密解算的天顶对流层延迟(ZTD)作为近似“真值”，分析比较Hopfield模型、传统BP模型和融合模型的计算精度。结果表明，Hopfield模型的均方根误差（RMSE）为35.31 mm，传统BP模型为30.34 mm，融合模型为23.31 mm。
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| 关 键 词： | 气象探空数据 顶对流层延迟 误差补偿 神经网络 融合模型 |
Research on Tropospheric Delay Model Based on Neural Network Model Error Compensation Technique |
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| CHEN Yang,HU Wusheng,YAN Yuxiang,LONG Fengyang,ZHANG Liang.Research on Tropospheric Delay Model Based on Neural Network Model Error Compensation Technique[J].Journal of Geodesy and Geodynamics,2018,38(6):577-580. |
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| Authors: | CHEN Yang HU Wusheng YAN Yuxiang LONG Fengyang ZHANG Liang |
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| Abstract: | Aiming at the low accuracy of the traditional tropospheric delay model, a high precision fusion model for the northern hemisphere is established based on the Hopfield model, using the neural network model error compensation technique. Taking the zenith tropospheric delay (ZTD) as the approximate “true value” of the meteorological sounding data of more than 120 observing stations in 2010 provided by the University of Wyoming, this paper analyzes and compares the Hopfield model, the traditional BP model, and the computational accuracy of the fusion model. The results show that the root mean square error (RMSE) of the Hopfield model is 35.31 mm, the RMSE of the traditional BP model is 30.34 mm, and the RMSE of the fusion model is 23.31 mm. |
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| Keywords: | meteorological sounding data zenith tropospheric delay error compensation neural network fusion model |
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