dc.contributor.author |
Ackermann, Etienne Rudolph
|
|
dc.contributor.author |
De Villiers, Johan Pieter
|
|
dc.contributor.author |
Cilliers, P.J.
|
|
dc.date.accessioned |
2012-02-16T14:33:34Z |
|
dc.date.available |
2012-02-16T14:33:34Z |
|
dc.date.issued |
2011-10-04 |
|
dc.description.abstract |
Two different implementations of Gaussian process (GP) models are proposed to
estimate the vertical total electron content (TEC) from dual frequency Global Positioning
System (GPS) measurements. The model falseness of GP and neural network models
are compared using daily GPS TEC data from Sutherland, South Africa, and it is shown
that the proposed GP models exhibit superior model falseness. The GP approach has
several advantages over previously developed neural network approaches, which
include seamless incorporation of prior knowledge, a theoretically principled method for
determining the much smaller number of free model parameters, the provision of estimates
of the model uncertainty, and a more intuitive interpretability of the model. |
en_US |
dc.description.uri |
http://www.agu.org/journals/jd/ |
en_US |
dc.identifier.citation |
Ackermann, E. R., J. P. de Villiers, and P. J. Cilliers (2011), Nonlinear dynamic systems modeling using Gaussian processes: Predicting ionospheric total electron content over South Africa, Journal of Geophysical Research, 116, A10303, DOI :10.1029/2010JA016375. |
en_US |
dc.identifier.issn |
0148-0227 |
|
dc.identifier.other |
10.1029/2010JA016375 |
|
dc.identifier.uri |
http://hdl.handle.net/2263/18129 |
|
dc.language.iso |
en |
en_US |
dc.publisher |
American Geophysical Union (AGU) |
en_US |
dc.rights |
An edited version of this paper was published by AGU. Copyright 2011 by the American Geophysical Union. This article is embargoed by the publisher until 04 April 2012. |
en_US |
dc.subject |
Nonlinear dynamic systems modeling |
en_US |
dc.subject |
Gaussian process (GP) |
en_US |
dc.subject |
Total electron content (TEC) |
en_US |
dc.subject |
Global Positioning System (GPS) measurements |
en_US |
dc.title |
Nonlinear dynamic systems modeling using Gaussian processes : predicting ionospheric total electron content over South Africa |
en_US |
dc.type |
Article |
en_US |