Predicting IGS RTS Corrections Using ARMA Neural Networks
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-06-22
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Kim, Mingyu& Kim, Jeongrae. 2015. Predicting IGS RTS Corrections Using ARMA Neural Networks. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1074895
Modern Language Association (MLA)
Kim, Mingyu& Kim, Jeongrae. Predicting IGS RTS Corrections Using ARMA Neural Networks. Mathematical Problems in Engineering No. 2015 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1074895
American Medical Association (AMA)
Kim, Mingyu& Kim, Jeongrae. Predicting IGS RTS Corrections Using ARMA Neural Networks. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1074895
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1074895