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dc.contributor.authorTunga, Mehmet Alperen_US
dc.contributor.authorDemiralp, Metinen_US
dc.date.accessioned2015-01-15T23:00:36Z
dc.date.available2015-01-15T23:00:36Z
dc.date.issued2006-01-01
dc.identifier.citationTunga, M. A. & Demiralp, M. (2006). Hybrid high dimensional model representation (HHDMR) on the partitioned data. Journal of Computational and Applied Mathematics, 185(1), 107-132. doi:10.1016/j.cam.2005.01.030en_US
dc.identifier.issn0377-0427
dc.identifier.issn1879-1778
dc.identifier.otherWOS:000232084200006
dc.identifier.urihttps://hdl.handle.net/11729/237
dc.identifier.urihttp://dx.doi.org/10.1016/j.cam.2005.01.030
dc.description.abstractA multivariate interpolation problem is generally constructed for appropriate determination of a multivariate function whose values are given at a finite number of nodes of a multivariate grid. One way to construct the solution of this problem is to partition the given multivariate data into low-variate data. High dimensional model representation (HDMR) and generalized high dimensional model representation (GHDMR) methods are used to make this partitioning. Using the components of the HDMR or the GHDMR expansions the multivariate data can be partitioned. When a cartesian product set in the space of the independent variables is given, the HDMR expansion is used. On the other band, if the nodes are the elements of a random discrete data the GHDMR expansion is used instead of HDMR. These two expansions work well for the multivariate data that have the additive nature. If the data have multiplicative nature then factorized high dimensional model representation (FHDMR) is used. But in most cases the nature of the given multivariate data and the sought multivariate function have neither additive nor multiplicative nature. They have a hybrid nature. So, a new method is developed to obtain better results and it is called hybrid high dimensional model representation (HHDMR). This new expansion includes both the HDMR (or GHDMR) and the FHDMR expansions through a hybridity parameter. In this work, the general structure of this hybrid expansion is given. It has tried to obtain the best value for the hybridity parameter. According to this value the analytical structure of the sought multivariate function can be determined via HHDMR.en_US
dc.language.isoengen_US
dc.publisherElsevier B.V.en_US
dc.relation.isversionof10.1016/j.cam.2005.01.030
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHigh dimensional model representationen_US
dc.subjectFactorized high dimensional model representationen_US
dc.subjectMultivariate functionsen_US
dc.subjectInterpolationen_US
dc.subjectMultidimensional problemsen_US
dc.subjectApproximationen_US
dc.subjectOptimizationen_US
dc.titleHybrid high dimensional model representation (HHDMR) on the partitioned dataen_US
dc.typearticleen_US
dc.description.versionPubliher's Versionen_US
dc.relation.journalJournal of Computational and Applied Mathematicsen_US
dc.contributor.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.contributor.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.contributor.authorID0000-0003-3551-4549
dc.identifier.volume185
dc.identifier.issue1
dc.identifier.startpage107
dc.identifier.endpage132
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthorTunga, Mehmet Alperen_US


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