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Bayesian Learning Using Gaussian Process for Gas Identification.
- Source :
-
IEEE Transactions on Instrumentation & Measurement . Jun2006, Vol. 55 Issue 3, p787-792. 6p. 2 Black and White Photographs, 1 Diagram, 2 Charts, 6 Graphs. - Publication Year :
- 2006
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189456
- Volume :
- 55
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Instrumentation & Measurement
- Publication Type :
- Academic Journal
- Accession number :
- 21030906
- Full Text :
- https://doi.org/10.1109/TIM.2006.873804