Monte-Carlo parameter uncertainty analysis under dynamical and operational measurement conditions

Kurt Barbé, Lee Gonzales Fuentes, Oscar Javier Olarte Rodriguez, Lieve Lauwers

Research output: Chapter in Book/Report/Conference proceedingConference paper

1 Citation (Scopus)

Abstract

For controlling, observing and optimizing engineering processes one needs often dedicated experiments. Unfortunately no measurement is exact such that deriving conclusions from a measurement campaign requires some caution. Hence, in order to control or optimize a certain parameter of interest, the measurement uncertainty of the parameter needs to be quantified. In the literature two methods are proposed to perform this task: analysis of the noise propagation or Bootstrap Monte-Carlo (BMC) methods. The first one is inaccessible for the layman user. The BMC is difficult to perform if noise sources are mutually correlated since all correlations need to be taken into account. We present a new direct measurement for parameter uncertainty which can be operated under correlated noise sources without the need of explicit knowledge or description of the correlation at hand.
Original languageEnglish
Title of host publicationIEEE International Instrumentation and Measurement Technology Conference
PublisherIEEE
Pages276-281
Number of pages6
ISBN (Print)978-1-4673-6386-0
Publication statusPublished - 15 May 2014
EventIEEE International Instrumentation and Measurement Technology Conference, I2MTC 2014 - Montevideo, Uruguay
Duration: 12 May 201415 May 2014

Conference

ConferenceIEEE International Instrumentation and Measurement Technology Conference, I2MTC 2014
CountryUruguay
CityMontevideo
Period12/05/1415/05/14

Keywords

  • Uncertainty
  • dynamical systems
  • monte-carlo analysis

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