dc.contributor.author Tunga, Mehmet Alper en_US dc.contributor.author Demiralp, Metin en_US dc.date.accessioned 2015-01-15T23:00:36Z dc.date.available 2015-01-15T23:00:36Z dc.date.issued 2006-01-01 dc.identifier.citation Tunga, 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.2005.01.030 en_US dc.identifier.issn 0377-0427 dc.identifier.issn 1879-1778 dc.identifier.other WOS:000232084200006 dc.identifier.uri https://hdl.handle.net/11729/237 dc.identifier.uri http://dx.doi.org/10.1016/j.cam.2005.01.030 dc.description.abstract A 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.iso eng en_US dc.publisher Elsevier B.V. en_US dc.relation.isversionof 10.1016/j.cam.2005.01.030 dc.rights info:eu-repo/semantics/closedAccess en_US dc.subject High dimensional model representation en_US dc.subject Factorized high dimensional model representation en_US dc.subject Multivariate functions en_US dc.subject Interpolation en_US dc.subject Multidimensional problems en_US dc.subject Approximation en_US dc.subject Optimization en_US dc.title Hybrid high dimensional model representation (HHDMR) on the partitioned data en_US dc.type article en_US dc.description.version Publiher's Version en_US dc.relation.journal Journal of Computational and Applied Mathematics en_US dc.contributor.department Işık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü en_US dc.contributor.department Işık University, Faculty of Engineering, Department of Computer Engineering en_US dc.contributor.authorID 0000-0003-3551-4549 dc.identifier.volume 185 dc.identifier.issue 1 dc.identifier.startpage 107 dc.identifier.endpage 132 dc.peerreviewed Yes en_US dc.publicationstatus Published en_US dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US dc.contributor.institutionauthor Tunga, Mehmet Alper en_US
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