Finding the best calibration points for a gas sensor array with support vector regression

Armin Shmilovici, Goekhan Bakir, Santiago Marco, Alexandre Perera

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

9 Scopus citations

Abstract

Electronic noses and gas alarm systems use chemical sensor arrays for the detection of gas mixtures. These sensing devices typically have a high degree of collinearity and non-linear responses which makes their calibration difficult. Support Vector Regression was used to select a minimal number of calibration points for a dataset generated from laboratory measurements of a twelve element Metal Oxide Sensor Array exposed to ternary mixtures of CO, CH4, and Ethanol. The results indicate that the prediction accuracy of the model generated with kernel regression methods is better than that of Partial Least Squares even when the number of calibration points is small.

Original languageEnglish
Title of host publication2004 2nd International IEEE Conference 'Intelligent Systems' - Proceedings
EditorsR.R. Yager, V.S. Sgurev, V.S. Jotsov, P.D. Koprinkova-Hristova
Pages174-177
Number of pages4
StatePublished - 1 Dec 2004
Event2004 2nd International IEEE Conference 'Intelligent Systems' - Proceedings - Varna, Bulgaria
Duration: 22 Jun 200424 Jun 2004

Publication series

Name2004 2nd International IEEE Conference 'Intelligent Systems' - Proceedings
Volume1

Conference

Conference2004 2nd International IEEE Conference 'Intelligent Systems' - Proceedings
Country/TerritoryBulgaria
CityVarna
Period22/06/0424/06/04

Keywords

  • Gas Sensor Calibration
  • Sensor Arrays
  • Support Vector Regression

ASJC Scopus subject areas

  • General Engineering

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