We previously used back propagation neural network (BPNN) with the meteorological factors of the receiver point to establish a model for predicting propagation delay of Loran-C signal over a short distance. Nevertheless, for a long propagation path, it is not proper to use only the meteorological factors of the receiver point. In this letter, a propagation delay prediction model that considers multiweather and multipoint was established by using a more suitable generalized regression neural network (GRNN) over a long distance. We first compared three meteorological factors of six points on the propagation path, and found that they have obvious differences. Then, a propagation delay prediction model based on three meteorological factors of the six points is established with GRNN. Finally, the further comparison shows that on the one hand, GRNN is more suitable to establish the prediction model of propagation delay than BPNN; on the other hand, more points on the propagation path are considered, and the prediction accuracy of the model is higher.

中文翻译：

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预测Loran-C信号长距离传播延迟的精度改进模型

我们以前使用反向传播神经网络（BPNN）和接收点的气象因素来建立一个模型，用于预测Loran-C信号在短距离内的传播延迟。但是，对于较长的传播路径，仅使用接收点的气象因素是不合适的。在这封信中，通过使用更适合的长距离广义广义回归神经网络（GRNN），建立了考虑多天气和多点的传播延迟预测模型。我们首先比较了传播路径上六个点的三个气象因素，发现它们之间存在明显差异。然后，利用GRNN建立了基于六个气象点的三个气象因子的传播时延预测模型。最后，进一步的比较表明，一方面，与BPNN相比，GRNN更适合用于建立传播延迟的预测模型。另一方面，考虑了传播路径上的更多点，模型的预测精度更高。