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Please use this identifier to cite or link to this item: https://rfos.fon.bg.ac.rs/handle/123456789/2407
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dc.creatorStanojević, Bogdana
dc.creatorStanojević, Milan
dc.date.accessioned2023-05-12T11:45:50Z-
dc.date.available2023-05-12T11:45:50Z-
dc.date.issued2022
dc.identifier.issn1877-0509
dc.identifier.urihttps://rfos.fon.bg.ac.rs/handle/123456789/2407-
dc.description.abstractThe role of the regression analysis is crucial in many disciplines. Addressing the fuzzy quadratic least square regression for observed data modeled by fuzzy numbers, we aim to emphasize how a methodology that does not fully comply to the extension principle may fail to predict fuzzy valued numbers. We also propose a solution approach that functions in full accordance to the extension principle, thus overcoming the shortcomings arisen from the practice of splitting the optimization of a fuzzy number in independent optimizations of its components.en
dc.publisherElsevier B.V.
dc.relationinfo:eu-repo/grantAgreement/MESTD/inst-2020/200151/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/inst-2020/200029/RS//
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceProcedia Computer Science
dc.subjectFuzzy regressionen
dc.subjectFuzzy numbersen
dc.subjectExtension principleen
dc.titleQuadratic least square regression in fuzzy environmenten
dc.typeconferenceObject
dc.rights.licenseBY
dc.citation.epage396
dc.citation.issueC
dc.citation.other214(C): 391-396
dc.citation.spage391
dc.citation.volume214
dc.identifier.doi10.1016/j.procs.2022.11.190
dc.identifier.fulltexthttp://prototype2.rcub.bg.ac.rs/bitstream/id/900/2403.pdf
dc.identifier.rcubconv_3755
dc.identifier.scopus2-s2.0-85146117390
dc.type.versionpublishedVersion
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.openairetypeconferenceObject-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:Radovi istraživača / Researchers’ publications
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