DC Field | Value | Language |
---|---|---|
dc.contributor.author | Soh, Woo Jin | ko |
dc.contributor.author | Kim, Heeyoung | ko |
dc.contributor.author | Yum, Bong-Jin | ko |
dc.date.accessioned | 2018-04-24T05:06:37Z | - |
dc.date.available | 2018-04-24T05:06:37Z | - |
dc.date.created | 2018-04-09 | - |
dc.date.created | 2018-04-09 | - |
dc.date.issued | 2018-04 | - |
dc.identifier.citation | ANNALS OF OPERATIONS RESEARCH, v.263, no.1-2, pp.69 - 91 | - |
dc.identifier.issn | 0254-5330 | - |
dc.identifier.uri | http://hdl.handle.net/10203/241305 | - |
dc.description.abstract | The 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.language | English | - |
dc.publisher | SPRINGER | - |
dc.subject | GREY RELATIONAL ANALYSIS | - |
dc.subject | DISCHARGE MACHINING PROCESS | - |
dc.subject | TAGUCHI METHOD | - |
dc.subject | PERFORMANCE-CHARACTERISTICS | - |
dc.subject | MULTIRESPONSE OPTIMIZATION | - |
dc.subject | GENETIC ALGORITHM | - |
dc.subject | ROBUST DESIGN | - |
dc.subject | MANUFACTURING PROCESS | - |
dc.subject | TURNING OPERATIONS | - |
dc.subject | NEURAL-NETWORK | - |
dc.title | Application of kernel principal component analysis to multi-characteristic parameter design problems | - |
dc.type | Article | - |
dc.identifier.wosid | 000427586000005 | - |
dc.identifier.scopusid | 2-s2.0-84929224696 | - |
dc.type.rims | ART | - |
dc.citation.volume | 263 | - |
dc.citation.issue | 1-2 | - |
dc.citation.beginningpage | 69 | - |
dc.citation.endingpage | 91 | - |
dc.citation.publicationname | ANNALS OF OPERATIONS RESEARCH | - |
dc.identifier.doi | 10.1007/s10479-015-1889-2 | - |
dc.contributor.localauthor | Kim, Heeyoung | - |
dc.contributor.localauthor | Yum, Bong-Jin | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Kernel principal component analysis | - |
dc.subject.keywordAuthor | Multiple performance characteristics | - |
dc.subject.keywordAuthor | Robust parameter design | - |
dc.subject.keywordAuthor | SN ratio | - |
dc.subject.keywordAuthor | Taguchi method | - |
dc.subject.keywordPlus | GREY RELATIONAL ANALYSIS | - |
dc.subject.keywordPlus | DISCHARGE MACHINING PROCESS | - |
dc.subject.keywordPlus | TAGUCHI METHOD | - |
dc.subject.keywordPlus | PERFORMANCE-CHARACTERISTICS | - |
dc.subject.keywordPlus | MULTIRESPONSE OPTIMIZATION | - |
dc.subject.keywordPlus | GENETIC ALGORITHM | - |
dc.subject.keywordPlus | ROBUST DESIGN | - |
dc.subject.keywordPlus | MANUFACTURING PROCESS | - |
dc.subject.keywordPlus | TURNING OPERATIONS | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
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