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| Titel |
Investigating the performance of neural network backpropagation algorithms for TEC estimations using South African GPS data |
| VerfasserIn |
J. B. Habarulema, L.-A. McKinnell |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
0992-7689
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| Digitales Dokument |
URL |
| Erschienen |
In: Annales Geophysicae ; 30, no. 5 ; Nr. 30, no. 5 (2012-05-24), S.857-866 |
| Datensatznummer |
250017227
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| Publikation (Nr.) |
 copernicus.org/angeo-30-857-2012.pdf |
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| Zusammenfassung |
| In this work, results obtained by investigating the application of
different neural network backpropagation training algorithms are presented.
This was done to assess the performance accuracy of each training algorithm
in total electron content (TEC) estimations using identical datasets in
models development and verification processes. Investigated
training algorithms are standard backpropagation (SBP), backpropagation with
weight delay (BPWD), backpropagation with momentum (BPM) term,
backpropagation with chunkwise weight update (BPC) and backpropagation for
batch (BPB) training. These five algorithms are inbuilt functions within the
Stuttgart Neural Network Simulator (SNNS) and the main objective was to find
out the training algorithm that generates the minimum error between the TEC derived from Global Positioning System (GPS) observations and the modelled TEC
data. Another investigated algorithm is the MatLab based
Levenberg-Marquardt backpropagation (L-MBP), which achieves convergence after
the least number of iterations during training. In this paper, neural network
(NN) models were developed using hourly TEC data (for 8 years: 2000–2007)
derived from GPS observations over a receiver
station located at Sutherland (SUTH) (32.38° S, 20.81° E), South
Africa. Verification of the NN models for all algorithms considered was
performed on both "seen" and "unseen" data. Hourly TEC values over SUTH
for 2003 formed the "seen" dataset. The "unseen" dataset consisted of
hourly TEC data for 2002 and 2008 over Cape Town (CPTN) (33.95° S,
18.47° E) and SUTH, respectively. The models' verification showed that all
algorithms investigated provide comparable results statistically, but differ
significantly in terms of time required to achieve convergence during
input-output data training/learning. This paper therefore provides a guide to
neural network users for choosing appropriate algorithms based on the
availability of computation capabilities used for research. |
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