Bayesian learning using Gaussian process for gas identification

Amine Bermak, Sofiane Brahim Belhouari

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

In this paper, a novel gas identification approach based on Gaussian process (GP) combined with principal components analysis is proposed. The effectiveness of this approach has been successfully demonstrated on an experimentally obtained dataset. Our aim is the identification of different gases with an array of commercial Taguchi gas sensors (TGS) as well as microelectronic gas sensors. The proposed approach is shown to outperform both K nearest neighbor (KNN) and multilayer perceptron (MLP) classifiers.

Original languageEnglish
Pages (from-to)787-792
Number of pages6
JournalIEEE Transactions on Instrumentation and Measurement
Volume55
Issue number3
DOIs
Publication statusPublished - Jun 2006
Externally publishedYes

Fingerprint

Chemical sensors
learning
Multilayer neural networks
Gases
gases
Microelectronics
Principal component analysis
Classifiers
self organizing systems
sensors
principal components analysis
classifiers
microelectronics

Keywords

  • Bayesian learning
  • Gas identification
  • Gas sensor array
  • Gaussian processes (GPs)
  • Pattern recognition

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

Cite this

Bayesian learning using Gaussian process for gas identification. / Bermak, Amine; Belhouari, Sofiane Brahim.

In: IEEE Transactions on Instrumentation and Measurement, Vol. 55, No. 3, 06.2006, p. 787-792.

Research output: Contribution to journalArticle

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