A Comparison of the Machine Learning Algorithm for Evaporation Duct Estimation

dc.contributor.authorYang, Chao
dc.coverage.issue2cs
dc.coverage.volume22cs
dc.date.accessioned2015-01-21T09:56:45Z
dc.date.available2015-01-21T09:56:45Z
dc.date.issued2013-06cs
dc.description.abstractIn this research, a comparison of the relevance vector machine (RVM), least square support vector machine (LSSVM) and the radial basis function neural network (RBFNN) for evaporation duct estimation are presented. The parabolic equation model is adopted as the forward propagation model, and which is used to establish the training database between the radar sea clutter power and the evaporation duct height. The comparison of the RVM, LSSVM and RBFNN for evaporation duct estimation are investigated via the experimental and the simulation studies, and the statistical analysis method is employed to analyze the performance of the three machine learning algorithms in the simulation study. The analysis demonstrate that the M profile of RBFNN estimation has a relatively good match to the measured profile for the experimental study; for the simulation study, the LSSVM is the most precise one among the three machine learning algorithms, besides, the performance of RVM is basically identical to the RBFNN.en
dc.formattextcs
dc.format.extent657-661cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2013, vol. 22, č. 2, s. 657-661. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/36898
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2013/13_02_0657_0661.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectMachine learning algorithmen
dc.subjectevaporation ducten
dc.subjectradar sea clutteren
dc.subjectparameter estimation.en
dc.titleA Comparison of the Machine Learning Algorithm for Evaporation Duct Estimationen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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