Cristin-resultat-ID: 1519537
Sist endret: 29. november 2017, 14:36
NVI-rapporteringsår: 2017
Resultat
Vitenskapelig Kapittel/Artikkel/Konferanseartikkel
2017

Regression analysis using a blending type spline construction

Bidragsytere:
  • Tatiana Kravetc
  • Børre Bang og
  • Rune Dalmo

Bok

Mathematical Methods for Curves and Surfaces: 9th International Conference, MMCS 2016 Tønsberg, Norway, June 23-28, 2016 Revised Selected Papers
ISBN:
  • 978-3-319-67885-6

Utgiver

Springer Publishing Company
NVI-nivå 1

Serie

Lecture Notes in Computer Science (LNCS)
ISSN 0302-9743
e-ISSN 1611-3349
NVI-nivå 1

Om resultatet

Vitenskapelig Kapittel/Artikkel/Konferanseartikkel
Publiseringsår: 2017
Hefte: 10521
Sider: 145 - 161
ISBN:
  • 978-3-319-67885-6
Open Access

Importkilder

Scopus-ID: 2-s2.0-85032664807

Klassifisering

Fagfelt (NPI)

Fagfelt: Informatikk og datateknikk
- Fagområde: Realfag og teknologi

Beskrivelse Beskrivelse

Tittel

Regression analysis using a blending type spline construction

Sammendrag

Regression analysis allows us to track the dynamics of change in measured data and to investigate their properties. A sufficiently good model allows us to predict the behavior of dependent variables with higher accuracy, and to propose a more precise data generation hypothesis. By using polynomial approximation for big data sets with complex dependencies we get piecewise smooth functions. One way to obtain a smooth spline representation of an entire data set is to use local curves and to blend them using smooth basis functions. This construction allows the computation of derivatives at any point on the spline. Properties such as tangent, velocity, acceleration, curvature and torsion can be computed, which gives us the opportunity to exploit these data in the subsequent analysis. We can adjust the accuracy of the approximation on the different segments of the data set by choosing a suitable knot vector. This article describes a new method for determining the number and location of the knot-points, based on changes in the Frenet frame. We present a method of implementation using generalized expo-rational B-splines (GERBS) for regression problems (in two and three variables) and we evaluate the accuracy of the model using comparison of the residuals.

Bidragsytere

Tatiana Kravetc

  • Tilknyttet:
    Forfatter
    ved Institutt for datateknologi og beregningsorienterte ingeniørfag ved UiT Norges arktiske universitet

Børre Bang

  • Tilknyttet:
    Forfatter
    ved Institutt for datateknologi og beregningsorienterte ingeniørfag ved UiT Norges arktiske universitet

Rune Dalmo

  • Tilknyttet:
    Forfatter
    ved Institutt for datateknologi og beregningsorienterte ingeniørfag ved UiT Norges arktiske universitet
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Resultatet er en del av Resultatet er en del av

Mathematical Methods for Curves and Surfaces: 9th International Conference, MMCS 2016 Tønsberg, Norway, June 23-28, 2016 Revised Selected Papers.

Floater, Michael S.; Lyche, Tom Johan; Mazure, Marie-Laurence; Mørken, Knut Martin; Schumaker, Larry L.. 2017, Springer Publishing Company. UJF(I, UIO, VUVitenskapelig antologi/Konferanseserie
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