Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42028
DC FieldValueLanguage
dc.contributor.authorCornejo-Bueno, L.en_US
dc.contributor.authorNieto-Borge, J.C.en_US
dc.contributor.authorGarcía-Díaz, P.en_US
dc.contributor.authorRodríguez, G.en_US
dc.contributor.authorSalcedo-Sanz, S.en_US
dc.date.accessioned2018-09-28T14:42:58Z-
dc.date.available2018-09-28T14:42:58Z-
dc.date.issued2016en_US
dc.identifier.issn0960-1481en_US
dc.identifier.urihttp://hdl.handle.net/10553/42028-
dc.description.abstractThis paper proposes a novel hybrid approach for feature selection in two different relevant problems for marine energy applications: significant wave height (. Hm0) and wave energy flux (P) prediction. Specifically, a hybrid Grouping Genetic Algorithm - Extreme Learning Machine approach (GGA-ELM) is proposed, in such a way that the GGA searches for several subsets of features, and the ELM provides the fitness of the algorithm, by means of its accuracy on Hm0 or P prediction. Since the GGA was specifically created for problems involving a number of groups, the proposed algorithm may be used to evolve different groups of features in parallel, which may improve the performance of the predictions obtained. After the feature selection process with the GGA-ELM, the final results are given by an ELM and also by a Support Vector Machine, both working on the best GGA groups obtained. The performance of the proposed system has been tested in a real problem of Hm0 and P prediction at the Western coast of the USA, obtaining good results.en_US
dc.languageengen_US
dc.relation.ispartofRenewable Energyen_US
dc.sourceRenewable Energy[ISSN 0960-1481],v. 97, p. 380-389en_US
dc.subject2510 Oceanografíaen_US
dc.subject.otherExtreme Learning Machinesen_US
dc.subject.otherGrouping genetic algorithm (GGA)en_US
dc.subject.otherMarine energyen_US
dc.subject.otherSignificant wave heighten_US
dc.subject.otherSupport vector machinesen_US
dc.subject.otherWave energy fluxen_US
dc.titleSignificant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm - Extreme Learning Machine approachen_US
dc.typeinfo:eu-repo/semantics/Articlees
dc.typeArticlees
dc.identifier.doi10.1016/j.renene.2016.05.094
dc.identifier.scopus84973131094-
dc.identifier.isi000380600500035-
dc.contributor.authorscopusid56732912600
dc.contributor.authorscopusid55663344400
dc.contributor.authorscopusid12790265900
dc.contributor.authorscopusid7203006681
dc.contributor.authorscopusid12789591800
dc.description.lastpage389-
dc.description.firstpage380-
dc.relation.volume97-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid3139230
dc.contributor.daisngid1867514
dc.contributor.daisngid5122163
dc.contributor.daisngid28190193
dc.contributor.daisngid140631
dc.contributor.wosstandardWOS:Cornejo-Bueno, L
dc.contributor.wosstandardWOS:Nieto-Borge, JC
dc.contributor.wosstandardWOS:Garcia-Diaz, P
dc.contributor.wosstandardWOS:Rodriguez, G
dc.contributor.wosstandardWOS:Salcedo-Sanz, S
dc.date.coverdateNoviembre 2016
dc.identifier.ulpgces
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.author.deptFísica marina y teledetección aplicada-
crisitem.author.deptIU de Estudios Ambientales y Recursos Naturales-
crisitem.author.deptDepartamento de Física-
crisitem.author.parentorgIU de Estudios Ambientales y Recursos Naturales-
crisitem.author.fullNameRodríguez Rodríguez, Germán Alejandro-
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