Predicting IGS RTS Corrections Using ARMA Neural Networks

Mingyu Kim and Jeongrae Kim

Mathematical Problems in Engineering, 2015, vol. 2015, 1-11

Abstract:

An autoregressive moving average neural network (ARMANN) model is applied to predict IGS real time service corrections. ARMA coefficients are determined by applying a neural network to IGS02 orbit/clock corrections. Other than the ARMANN, the polynomial and ARMA models are tested for comparison. An optimal order of each model is determined by fitting the model to the correction data. The data fitting period for training the models is 60 min. and the prediction period is 30 min. The polynomial model is good for the fitting but bad for the prediction. The ARMA and ARMANN have a similar level of accuracies, but the RMS error of the ARMANN is smaller than that of the ARMA. The RMS error of the ARMANN is 0.046 m for the 3D orbit correction and 0.070 m for the clock correction. The difference between the ARMA and ARMANN models becomes significant as the prediction time is increased.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:851761

DOI: 10.1155/2015/851761

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