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Application of General Regression Neural Network to the Prediction of LOD Change

机译:广义回归神经网络在LOD变化预测中的应用

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摘要

Traditional methods for predicting the change in length of day (LOD change) are mainly based on some linear models, such as the least square model and autoregression model, etc. However, the LOD change comprises complicated non-linear factors and the prediction effect of the linear models is always not so ideal. Thus, a kind of non-linear neural network - general regression neural network (GRNN) model is tried to make the prediction of the LOD change and the result is compared with the predicted results obtained by taking advantage of the BP (back propagation) neural network model and other models. The comparison result shows that the application of the GRNN to the prediction of the LOD change is highly effective and feasible.
机译:传统的预测日长变化(LOD变化)的方法主要基于线性模型,例如最小二乘模型和自回归模型等。但是,LOD变化包含复杂的非线性因素以及预测的影响线性模型总是不那么理想。因此,尝试使用一种非线性神经网络-通用回归神经网络(GRNN)模型对LOD变化进行预测,并将结果与​​利用BP(反向传播)神经获得的预测结果进行比较。网络模型和其他模型。比较结果表明,将GRNN应用于LOD变化的预测是高度有效和可行的。

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