Publication
Title
Multivariate data fitting with error control
Author
Abstract
We show how a recently developed multivariate data fitting technique enables to solve a variety of scientific computing problems in filtering, queueing, networks, metamodelling, computational finance, graphics, and more. We can capture linear as well as nonlinear phenomena because the method uses a generalized multivariate rational model. The technique is a refinement of the basic ideas developed in Salazar et al. (Numer Algorithms 45:375388, 2007. https://doi.org/10.1007/s11075-007-9077-3) and interpolates interval data. Intervals allow to take the inherent data error in measurements and simulation into consideration, whilst guaranteeing an upper bound on the tolerated range of uncertainty. The latter is the main difference with a best approximation or least squares technique which does as well as it can, but without respecting an a priori imposed threshold on the approximation error. Compared to the best approximations, the interval interpolant is relatively easy to compute. In applications where industry standards need to be guaranteed, the interval interpolation technique may be a valuable alternative.
Language
English
Source (journal)
Bit : numerical mathematics. - Lund
Publication
Lund : 2019
ISSN
0006-3835 [print]
1572-9125 [online]
Volume/pages
59 :1 (2019) , p. 35-55
ISI
000460614200002
Full text (Publisher's DOI)
Full text (open access)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 01.10.2018
Last edited 06.09.2021
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