Comparison of artificial neural network and multilinear regression analysis models in estimation of pulp flow speed from low coherence Doppler flowmetry measurement data

Manne Hannula*, Erkki Alarousu, Tuukka Prykäri, Risto Myllylä

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Low Coherence Doppler Flowmetry (LCDF) measurement produces a signal, which frequency domain characteristics are in connection to the speed of the flow. In this study performances of Artificial Neural Network (ANN) and Multilinear Regression (MLR) methods in prediction of pulp flow speed from the LCDF measurement data were compared. In the study the pulp flow speed was estimated distinctly from consecutive frequency bands of the LCDF data with both methods. The smallest estimation error in flow speed with the ANN method was 20% and with the MLR method 30%, depending on the selected frequency band. The results indicate the relationship between characteristics of the LCDF measurement and pulp flow speed includes remarkable number of nonlinear components. The result is in line with theoretical calculations about the Doppler shifts occurrence in the LCDF data.

Original languageEnglish (US)
Title of host publicationAdvanced Laser Technologies 2006
Volume6606
DOIs
StatePublished - 2007
Externally publishedYes
EventAdvanced Laser Technologies 2006 - Brasov, Romania
Duration: Sep 8 2006Sep 12 2006

Other

OtherAdvanced Laser Technologies 2006
CountryRomania
CityBrasov
Period09/8/0609/12/06

Keywords

  • LDF
  • Nonlinear model
  • Pulp and paper

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

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