Comparison of L-1 Norm and L-2 Norm Minimisation Methods in Trigonometric Levelling Networks

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dc.contributor.authorInal, Cevat
dc.contributor.authorYetkin, Mevlut
dc.contributor.authorBulbul, Sercan
dc.contributor.authorBilgen, Burhaneddin
dc.date.accessioned2020-03-26T19:53:11Z
dc.date.available2020-03-26T19:53:11Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe most widely-used parameter estimation method today is the L-2 norm minimisation method known as the Least Squares Method (LSM). The solution to the L-2 norm minimisation method is always unique and is easily computed. This method distributes errors and is sensitive to outlying measurements. Therefore, a robust technique known as the Least Absolute Values Method (LAVM) might be used for the detection of outliers and for the estimation of parameters. In this paper, the formulation of the L-1 norm minimisation method will be explained and the success of the method in the detection of gross errors will be investigated in a trigonometric levelling network.en_US
dc.identifier.doi10.17559/TV-20160809163639en_US
dc.identifier.endpage221en_US
dc.identifier.issn1330-3651en_US
dc.identifier.issn1848-6339en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage216en_US
dc.identifier.urihttps://dx.doi.org/10.17559/TV-20160809163639
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36430
dc.identifier.volume25en_US
dc.identifier.wosWOS:000433290300031en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherUNIV OSIJEK, TECH FACen_US
dc.relation.ispartofTEHNICKI VJESNIK-TECHNICAL GAZETTEen_US
dc.relation.publicationcategoryDiğeren_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectlinear programmingen_US
dc.subjectmeasurements with gross erroren_US
dc.subjectsimplex methoden_US
dc.subjecttrigonometric levelling networksen_US
dc.titleComparison of L-1 Norm and L-2 Norm Minimisation Methods in Trigonometric Levelling Networksen_US
dc.typeReviewen_US

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