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dc.contributor.authorFernández-Lozano, Carlos
dc.contributor.authorCedrón, Francisco
dc.contributor.authorRivero, Daniel
dc.contributor.authorDorado, Julián
dc.contributor.authorAndrade-Garda, José Manuel
dc.contributor.authorPazos, A.
dc.contributor.authorGestal, M.
dc.date.accessioned2016-10-28T08:08:23Z
dc.date.issued2016-06
dc.identifier.citationFernández-Lozano C, Cedrón F, Rivero D, Dorado J. Andrade-Garda JM, Pazos A, Gestal M. Using genetic algorithms to improve support vector regression in the analysis of atomic spectra of lubricant oils. Engineering Computations. 2016;33(4):995-1005es_ES
dc.identifier.urihttp://hdl.handle.net/2183/17500
dc.description.abstract[Abstract] Purpose – The purpose of this paper is to assess the quality of commercial lubricant oils. A spectroscopic method was used in combination with multivariate regression techniques (ordinary multivariate multiple regression, principal components analysis, partial least squares, and support vector regression (SVR)). Design/methodology/approach – The rationale behind the use of SVR was the fuzzy characteristics of the signal and its inherent ability to find nonlinear, global solutions in highly complex dimensional input spaces. Thus, SVR allows extracting useful information from calibration samples that makes it possible to characterize physical-chemical properties of the lubricant oils. Findings – A dataset of 42 spectra measured from oil standards was studied to assess the concentration of copper into the oils and, thus, evaluate the wearing of the machinery. It was found that the use of SVR was very advantageous to get a regression model. Originality/value – The use of genetic algorithms coupled to SVR was considered in order to reduce the time needed to find the optimal parameters required to get a suitable prediction model.es_ES
dc.language.isoenges_ES
dc.publisherEmeraldes_ES
dc.relation.urihttp://dx.doi.org/10.1108/EC-03-2015-0062es_ES
dc.subjectGenetic algorithmses_ES
dc.subjectLubricant oilses_ES
dc.subjectSupportes_ES
dc.subjectVector regressiones_ES
dc.titleUsing Genetic Algorithms to Improve Support Vector Regression in the Analysis of Atomic Spectra of Lubricant Oilses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/embargoedAccesses_ES
dc.date.embargoEndDate2017-06-01es_ES
dc.date.embargoLift2017-06-01
UDC.journalTitleEngineering Computationses_ES
UDC.volume33es_ES
UDC.issue4es_ES
UDC.startPage995es_ES
UDC.endPage1005es_ES


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