Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/5001
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dc.contributor.authorallDuka, B.; Department of Physics, Faculty of Natural Sciences, University of Tiranaen
dc.contributor.authorallHyka, N.; Department of Physics, Faculty of Natural Sciences, University of Tiranaen
dc.date.accessioned2009-04-01T10:04:41Zen
dc.date.available2009-04-01T10:04:41Zen
dc.date.issued2008-10en
dc.identifier.urihttp://hdl.handle.net/2122/5001en
dc.description.abstractstudy their time evolution in years. In order to find the best NN for the time predictions, we tested many different kinds of NN and different ways of their training, when the inputs and targets are long annual time series of synthetic geomagnetic field values. The found NN was used to predict the values of the annual means of the geomagnetic field components beyond the time registration periods of a Geomagnetic Observatory. In order to predict a time evolution of the global field over the Earth, we considered annual means of 105 Geomagnetic Observatories, chosen to have more than 30 years registration (1960.5-2005.5) and to be well distributed over the Earth. Using the NN technique, we created 137 «virtual geomagnetic observatories» in the places where real Geomagnetic Observatories are missing. Then, using NN, we predicted the time evolution of the three components of the global geomagnetic field beyond 2005.5.en
dc.language.isoEnglishen
dc.relation.ispartofAnnals of Geophysicsen
dc.relation.ispartofseries5-6/51 (2008)en
dc.subjectGeomagnetic Fielden
dc.subjectGeomagnetic Observatoryen
dc.subjectNeural Networks (NN)en
dc.subjecttime seriesen
dc.subjecttime predictionen
dc.titleUsing neural networks to study the geomagnetic field evolutionen
dc.typearticleen
dc.description.statusPublisheden
dc.type.QualityControlPeer-revieweden
dc.description.pagenumber755-767en
dc.subject.INGV04. Solid Earth::04.05. Geomagnetism::04.05.99. General or miscellaneousen
dc.relation.referencesAgg arwal, K.K., Y. Sing h, P. Chandra and M. Puri (2005): Bayesian Regularization in a Neural Network Model, Journal of Computer Sciences, 1 (4), 505- 509. Demuth, H. and M. Beale (2004): Matlab Neural network Toolbox User’s Guide, Version 4, The MathWorks inc., Natick, MA. Duka, B. (2005): Modeling the geomagnetic field at different observatories with nonlinear dynamical system of equations, in the 10th Scientific Assembly of the International Association of Geomagnetism and Aeronomy, (July 18-29, 2005, Toulouse, France). Frank, R.J., N. Dave and S.P. Hunt (2001): Time Series Prediction and Neural Networks, Journal of Intelligent and Robotic Systems, 31(1-3), 91-103. Hong re, L., P. Sailhac, M. Alexandrescu and J. Dubois (1999): Nonlinear and multifractal approaches of the geomagnetic field, Phys. Earth Planet. Inter., 110, 157-190. Jackson, A., A.R.T. Jonkers and M.R. Walker (2000): Four centuries of geomagnetic secular variation from historical records by Phil. Trans. R. Soc. Lond. A, 358, 957-990. Kugblenu, K., S. Taguchi and T. Okuzawa (1999): Prediction of the geomagnetic storm associated Dst index using an artificial neural network algorithm, Earth Planets Space, 51, 307-313.en
dc.description.journalTypeJCR Journalen
dc.description.fulltextopenen
dc.contributor.authorDuka, B.en
dc.contributor.authorHyka, N.en
dc.contributor.departmentDepartment of Physics, Faculty of Natural Sciences, University of Tiranaen
dc.contributor.departmentDepartment of Physics, Faculty of Natural Sciences, University of Tiranaen
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairetypearticle-
item.grantfulltextopen-
crisitem.author.deptDepartment of Physics, Faculty of Natural Sciences, University of Tirana, Albania-
crisitem.author.deptDepartment of Physics, Faculty of Natural Sciences, University of Tirana, Albania-
crisitem.author.orcid0000-0002-2014-1316-
crisitem.classification.parent04. Solid Earth-
Appears in Collections:Annals of Geophysics
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