Predicting the corrections for the polish timescale UTC(PL) using GMDH and GRNN neural networks

L. Sobolewski
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引用次数: 8

Abstract

The article presents the results of research on the prediction of the polish timescale UTC(PL) based on GMDH and GRNN neural networks, which were compared with the results obtained in the GUM using the analytical linear regression method. The lowest values of the prediction error was obtained for GMDH neural network for time series analysis methods and data prepared on the basis of time series ts1. These results were significantly better than the prediction error values obtained in GUM using analytical linear regression method. In the case of GRNN neural network prediction errors obtained using the regression method and data prepared on the basis of time series ts2 are very close to the values of prediction error obtained in the GUM. However, for data prepared on the basis of time series ts1 reached a very high value.
利用GMDH和GRNN神经网络预测波兰时间尺度UTC(PL)的修正
本文介绍了基于GMDH和GRNN神经网络的抛光时标UTC(PL)预测的研究结果,并与使用解析线性回归方法在GUM中得到的结果进行了比较。对于时间序列分析方法和基于时间序列ts1制备的数据,GMDH神经网络的预测误差最小。这些结果明显优于用解析线性回归方法在GUM中得到的预测误差值。在GRNN神经网络的情况下,使用回归方法得到的预测误差和基于时间序列ts2准备的数据与GUM得到的预测误差非常接近。然而,对于基于时间序列准备的数据,ts1达到了非常高的值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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