Title :
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.
fDate :
6/1/2006 12:00:00 AM
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;
Journal_Title :
Instrumentation and Measurement, IEEE Transactions on
DOI :
10.1109/TIM.2006.873804
