Error propagation through generalized high dimensional model representation for data partitioning
Küçük Resim Yok
Tarih
2004
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley-V C H Verlag GMBH
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In many circumstances the explicit form of a multivariate function is not known; rather a finite number of data is listed from some physical experiments. In such cases a function can be constructed only by imposing some analytical structures containing a finite number of adjustable parameters to fit the function with the given values at some specified points. This means interpolation. The given data is collected or produced by some devices or means which may cause unavoidable errors. This results in an uncertainty band for each datum. The propagation of these errors through the interpolation is the focus of this work. It uses a new form of a partitioning technique called Generalized High Dimensional Model Representation (GHDMR). GHDMR is a divide-and-conquer approach starting from a constant component and proceeding upto high variate terms, univariate, bivariate and so on in the representation. The representation is truncated by keeping only constant and univariate terms for approximation. In other words just a single N variate problem is approximated by N univariate problem.
Açıklama
Anahtar Kelimeler
Kaynak
International Conference of Numerical Analysis and Applied Mathematics
WoS Q Değeri
N/A
Scopus Q Değeri
Cilt
Sayı
Künye
Tunga, M. A. & Demiralp, M. (2004). Error propagation through generalized high dimensional model representation for data partitioning, Presented at the International Conference on Numerical Analysis and Applied Mathematics, 406-409.
