Bayesian learning using Gaussian process for gas identification

Author

Bermak, Amine ; Belhouari, Sofiane Brahim

Author_Institution

Dept. of Electr. & Electron. Eng., Hong Kong Univ. of Sci. & Technol.

Volume

55

Issue

3

fYear

2006

fDate

6/1/2006 12:00:00 AM

Firstpage

787

Lastpage

792

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

Keywords

Gaussian processes; array signal processing; belief networks; gas sensors; learning (artificial intelligence); principal component analysis; Bayesian learning; Gaussian process; K nearest neighbor; KNN; MLP classifiers; TGS; Taguchi gas sensors; gas identification; microelectronic gas sensors; multilayer perceptron classifiers; principal components analysis; Bayesian methods; Gas detectors; Gases; Gaussian processes; Microelectronics; Nearest neighbor searches; Neural networks; Pattern recognition; Principal component analysis; Sensor arrays; Bayesian learning; Gaussian processes (GPs); gas identification; gas sensor array; pattern recognition;

fLanguage

English

Journal_Title

Instrumentation and Measurement, IEEE Transactions on

Publisher

ieee

ISSN

0018-9456

Type

jour

DOI

10.1109/TIM.2006.873804

Filename

1634868