Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/975
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dc.contributor.authorallCander, L. R.; Rutherford Appleton Laboratory, Chilton, Didcot, Oxon, U.K.en
dc.contributor.authorallMilosavljevic´, M. M.; Faculty of Electrical Engineering, University of Belgrade, Belgrade, Serbia and Montenegroen
dc.contributor.authorallTomasevic´, S.; Faculty of Electrical Engineering, University of Belgrade, Belgrade, Serbia and Montenegroen
dc.date.accessioned2006-02-23T10:23:45Zen
dc.date.available2006-02-23T10:23:45Zen
dc.date.issued2003en
dc.identifier.urihttp://hdl.handle.net/2122/975en
dc.description.abstractIn this work we further refine and improve the neural network based ionospheric characteristic's foF2 predictor, which is actually a neural network autoregressive model with additional input signals (NNARX). Our analysis is focused on choice of X parts of NNARX model in order to capture middle and long term dependencies. Daily distribution of prediction error suggests need for structural changes of the neural network model, as well as adaptation of running average lengths used for determination of X inputs. Generalisation properties of proposed neural predictor are improved by carefully designed pruning procedure with additional regularisation term in criterion function. Some results from the NNARX model are presented to illustrate the feasibility of using such a model as ionospheric storm forecasting technique.en
dc.format.extent127703 bytesen
dc.format.mimetypeapplication/pdfen
dc.language.isoEnglishen
dc.publisher.nameINGVen
dc.relation.ispartofAnnals of Geophysicsen
dc.relation.ispartofseries4/46 (2003)en
dc.subjectprediction and forecastingen
dc.subjectneural networksen
dc.subjectionospheric storms modellingen
dc.subjectspace weatheren
dc.titleIonospheric storm forecasting technique by artificial neural networken
dc.typearticleen
dc.description.statusPublisheden
dc.type.QualityControlPeer-revieweden
dc.subject.INGV01. Atmosphere::01.02. Ionosphere::01.02.03. Forecastsen
dc.subject.INGV01. Atmosphere::01.02. Ionosphere::01.02.06. Instruments and techniquesen
dc.description.journalTypeJCR Journalen
dc.description.fulltextopenen
dc.contributor.authorCander, L. R.en
dc.contributor.authorMilosavljevic´, M. M.en
dc.contributor.authorTomasevic´, S.en
dc.contributor.departmentRutherford Appleton Laboratory, Chilton, Didcot, Oxon, U.K.en
dc.contributor.departmentFaculty of Electrical Engineering, University of Belgrade, Belgrade, Serbia and Montenegroen
dc.contributor.departmentFaculty of Electrical Engineering, University of Belgrade, Belgrade, Serbia and Montenegroen
item.openairetypearticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
crisitem.author.deptRutherford Appleton Laboratory, Chilton, Didcot, Oxon, U.K.-
crisitem.author.deptFaculty of Electrical Engineering, University of Belgrade, Belgrade, Serbia and Montenegro-
crisitem.author.deptFaculty of Electrical Engineering, University of Belgrade, Belgrade, Serbia and Montenegro-
crisitem.author.orcid0000-0002-7263-5043-
crisitem.classification.parent01. Atmosphere-
crisitem.classification.parent01. Atmosphere-
Appears in Collections:Annals of Geophysics
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