Application of kernel principal component analysis to multi-characteristic parameter design problems

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dc.contributor.authorSoh, Woo Jinko
dc.contributor.authorKim, Heeyoungko
dc.contributor.authorYum, Bong-Jinko
dc.date.accessioned2018-04-24T05:06:37Z-
dc.date.available2018-04-24T05:06:37Z-
dc.date.created2018-04-09-
dc.date.created2018-04-09-
dc.date.issued2018-04-
dc.identifier.citationANNALS OF OPERATIONS RESEARCH, v.263, no.1-2, pp.69 - 91-
dc.identifier.issn0254-5330-
dc.identifier.urihttp://hdl.handle.net/10203/241305-
dc.description.abstractThe Taguchi method for robust parameter design traditionally deals with single characteristic parameter design problems. Extending the Taguchi method to the case of multi-characteristic parameter design (MCPD) problems requires an overall evaluation of multiple characteristics, for which the principal component analysis (PCA) has been frequently used. However, since the PCA is based on a linear transformation, it may not be effectively used for the data with complicated nonlinear structures. This paper develops a kernel PCA-based method that allows capturing nonlinear relationships among multiple characteristics in constructing a single aggregate performance measure. Applications of the proposed method to simulated and real experimental data show the advantages of the kernel PCA over the original PCA for solving MCPD problems.-
dc.languageEnglish-
dc.publisherSPRINGER-
dc.subjectGREY RELATIONAL ANALYSIS-
dc.subjectDISCHARGE MACHINING PROCESS-
dc.subjectTAGUCHI METHOD-
dc.subjectPERFORMANCE-CHARACTERISTICS-
dc.subjectMULTIRESPONSE OPTIMIZATION-
dc.subjectGENETIC ALGORITHM-
dc.subjectROBUST DESIGN-
dc.subjectMANUFACTURING PROCESS-
dc.subjectTURNING OPERATIONS-
dc.subjectNEURAL-NETWORK-
dc.titleApplication of kernel principal component analysis to multi-characteristic parameter design problems-
dc.typeArticle-
dc.identifier.wosid000427586000005-
dc.identifier.scopusid2-s2.0-84929224696-
dc.type.rimsART-
dc.citation.volume263-
dc.citation.issue1-2-
dc.citation.beginningpage69-
dc.citation.endingpage91-
dc.citation.publicationnameANNALS OF OPERATIONS RESEARCH-
dc.identifier.doi10.1007/s10479-015-1889-2-
dc.contributor.localauthorKim, Heeyoung-
dc.contributor.localauthorYum, Bong-Jin-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorKernel principal component analysis-
dc.subject.keywordAuthorMultiple performance characteristics-
dc.subject.keywordAuthorRobust parameter design-
dc.subject.keywordAuthorSN ratio-
dc.subject.keywordAuthorTaguchi method-
dc.subject.keywordPlusGREY RELATIONAL ANALYSIS-
dc.subject.keywordPlusDISCHARGE MACHINING PROCESS-
dc.subject.keywordPlusTAGUCHI METHOD-
dc.subject.keywordPlusPERFORMANCE-CHARACTERISTICS-
dc.subject.keywordPlusMULTIRESPONSE OPTIMIZATION-
dc.subject.keywordPlusGENETIC ALGORITHM-
dc.subject.keywordPlusROBUST DESIGN-
dc.subject.keywordPlusMANUFACTURING PROCESS-
dc.subject.keywordPlusTURNING OPERATIONS-
dc.subject.keywordPlusNEURAL-NETWORK-
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