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Prediction of Earth orientation parameters by artificial neural networks |
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| Authors: | H Schuh M Ulrich D Egger J Müller W Schwegmann |
| Institution: | Institut für Geod?sie und Geophysik, Technische Universit?t Wien, Gusshausstrasse 27–29, 1040 Wien, Austria e-mail: hschuh@luna.tuwien.ac.at; Tel.: +43-1-58801-12860; Fax: +43-1-58801-12896, AT Lehrstuhl für Photogrammetrie und Fernerkundung, Technische Universit?t München, Arcisstrasse 21, 80290 München, Germany e-mail: markus.ulrich@bv.tum.de; Tel.: +49-89-289-22643; Fax: +49-89-280-9573, DE Forschungseinrichtung Satellitengeod?sie, Technische Universit?t München, Arcisstrasse 21, 80333 München, Germany e-mail: dieter.egger@bv.tum.de; Tel.: +49-89-289-23183; Fax: +49-89-289-23178, DE Institut für Astronomische und Physikalische Geod?sie, Technische Universit?t München, Arcisstrasse 21, 80333 München, Germany. Now at: Universit?t Hannover, Institut für Erdmessung, Schneiderberg 50, 30167 Hannover, Germany e-mail: mueller@ife.uni-hannover.de; Tel.: +49 (0)511/762-3362; Fax: +49 (0)511/762-4068; http://www.ife.uni-hannover.de, DE CNR Istituto Di Radioastronomia, Via P. Gobetti, 101, 40129 Bologna, Italy e-mail: schwegma@ira.bo.cnr.it; Tel.: +39-051-6399383; Fax: +39-051-6399431, IT |
| Abstract: | Earth orientation parameters (EOPs) polar motion and length of day (LOD), or UT1–UTC] were predicted by artificial neural networks. EOP series from various sources, e.g. the C04 series from the International Earth Rotation Service and the re-analysis optical astrometry series based on the HIPPARCOS frame, served for training the neural network for both short-term and long-term predictions. At first, all effects which can be described by functional models, e.g. effects of the solid Earth tides and the ocean tides or seasonal atmospheric variations of the EOPs, were removed. Only the differences between the modeled and the observed EOPs, i.e. the quasi-periodic and irregular variations, were used for training and prediction. The Stuttgart neural network simulator, which is a very powerful software tool developed at the University of Stuttgart, was applied to construct and to validate different types of neural networks in order to find the optimal topology of the net, the most economical learning algorithm and the best procedure to feed the net with data patterns. The results of the prediction were analyzed and compared with those obtained by other methods. The accuracy of the prediction is equal to or even better than that by other prediction methods. Received: 6 February 2001 / Accepted: 23 October 2001 |
| Keywords: | : Earth Rotation – Prediction – Neural Networks |
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