Robust surrogate measurement correction using generalised additive model

Karl Ropkins, Hu Li, Gary Hawley, Haibo Chen, James Tate, Gordon Andrews, Margaret Bell

Research output: Contribution to journalArticle

Abstract

The generalised additive model (GAM) was developed as a prognostic tool for the investigation of data set trends. However, it is also proposed as a viable framework for the development of surrogate measurement corrections for instrumental data sets. This hypothesis is supported by an example application, the development of a robust vehicle exhaust flow measurement correction for a flow meter with a known measurement error.
Original languageEnglish
Pages (from-to)164-169
JournalChemometrics and Intelligent Laboratory Systems
Volume95
Issue number2
DOIs
Publication statusPublished - 2009

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Flow measurement
Measurement errors

Keywords

  • Generalised additive model (GAM)
  • Vehicle exhaust flow
  • Measurement correction
  • Pitot exhaust flow meter

Cite this

Robust surrogate measurement correction using generalised additive model. / Ropkins, Karl; Li, Hu; Hawley, Gary; Chen, Haibo; Tate, James; Andrews, Gordon; Bell, Margaret.

In: Chemometrics and Intelligent Laboratory Systems, Vol. 95, No. 2, 2009, p. 164-169.

Research output: Contribution to journalArticle

Ropkins, Karl ; Li, Hu ; Hawley, Gary ; Chen, Haibo ; Tate, James ; Andrews, Gordon ; Bell, Margaret. / Robust surrogate measurement correction using generalised additive model. In: Chemometrics and Intelligent Laboratory Systems. 2009 ; Vol. 95, No. 2. pp. 164-169.
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