中文摘要：

目前,经验对流层天顶延迟（ZTD）模型已经有了飞速的发展,因为它们在使用时无需任何测量的实时地面气象数据,这给GNSS用户提供了极大方便。神经网络技术在实测参数型的ZTD建模中已经取得了一定的成果。与此同时,国内虽然有学者构建了神经外网络的经验ZTD模型,其最大的缺点是忽略了ZTD时间变化且只能单独预报ZTD。本文针对这些缺点构建了优化的神经网络经验ZTD模型。试验结果表明,本文提出的神经网络模型可以分别预报天顶干延迟ZHD和天顶湿延迟ZWD,且具有良好的精度：ZHD的Bias和RMSE分别为-3.7和19.8 mm;ZWD的Bias和RMSE分别为-0.6和34.2 mm。本文的神经网络模型预报的ZHD和ZWD的精度均与目前世界著名的GPT2w格网模型相当。另外,与GPT2w模型相比较,神经网络模型最大的优点就是无需庞大的预存格网数据作为输入,在使用时仅需要知道一个训练好的神经网络即可,该特点为GNSS用户提供了极大的方便。

英文摘要：

Recently,blind tropospheric zenith delay（ ZTD） models have been developed rapidly since they don＇t require any measure surface meteorological data,giving GNSS users great convenience. Neural network technology for ZTD models based on sited measured data has made some achievements. Meanwhile,some neural network models for blind ZTD models have been built,but they have some drawbacks： it ignore the ZTD variation with the time and can only forecast ZTD. In view of these shortcomings,this paper constructs an optimization of neural network model of a blind ZTD model. Results show that the proposed neural network models can forecast both ZHD and ZWD respectively and are with good accuracy： for ZHD,BIAS and RMSE are 2.5 mm and 20.6 mm respectively; for ZWD BIAS and RMSE are 2.4 mm and 35.7 mm respectively. In this paper,the ZHD and ZWD precision of the neural network models are also with the world famous blind model- the GPT2 w. In addition,compared with the GPT2 w,the neural network models in this study have the biggest advantages of usage without large grid data as reserved data but just need to know when to use a trained neural network,whose characteristics provide GNSS users with great convenience.