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Ultra short-term prediction of pole coordinates via combination of empirical mode decomposition and neural networks / Yu Lei in Artificial satellites, vol 51 n° 4 (December 2016)  

Public [article]  inArtificial satellites > vol 51 n° 4 (December 2016) . - pp 149 – 161 Titre : Ultra short-term prediction of pole coordinates via combination of empirical mode decomposition and neural networks Type de document : Article/Communication Auteurs : Yu Lei, Auteur ; Danning Zhao, Auteur ; Hongbing Cai, Auteur Année de publication : 2016 Article en page(s) : pp 149 – 161 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] filtre passe-bas [Termes IGN] fonction de base radiale [Termes IGN] mouvement du pôle [Termes IGN] oscillation [Termes IGN] prévision à court terme [Termes IGN] réseau neuronal artificiel [Termes IGN] terme de Chandler Résumé : (auteur) It was shown in the previous study that the increase of pole coordinates prediction error for about 100 days in the future is mostly caused by irregular short period oscillations. In this paper, the ultra short-term prediction of pole coordinates is studied for 10 days in the future by means of combination of empirical mode decomposition (EMD) and neural networks (NN), denoted EMD-NN. In the algorithm, EMD is employed as a low pass filter for eliminating high frequency signals from observed pole coordinates data. Then the annual and Chandler wobbles are removed a priori from pole coordinates data with high frequency signals eliminated. Finally, the radial basis function (RBF) networks are used to model and predict the residuals. The prediction performance of the EMD-NN approach is compared with that of the NN-only solution and the prediction methods and techniques involved in the Earth orientation parameters prediction comparison campaign (EOP PCC). The results show that the prediction accuracy of the EMD-NN algorithm is better than that of the NN-only solution and is also comparable with that of the other existing prediction method and techniques. Numéro de notice : A2016-977 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/arsa-2016-0013 En ligne : https://doi.org/10.1515/arsa-2016-0013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83688 [article]