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Sensors 2018, 18(2), 388; https://doi.org/10.3390/s18020388

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing

1
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
2
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China
3
High Tech Department, China International Engineering Consulting Corporation, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Received: 21 November 2017 / Revised: 8 January 2018 / Accepted: 26 January 2018 / Published: 29 January 2018
(This article belongs to the Special Issue Artificial Olfaction and Taste)
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Abstract

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods. View Full-Text
Keywords: electronic nose; feature extraction; multiple kernel learning; weighted kernels Fisher discriminant analysis; classification electronic nose; feature extraction; multiple kernel learning; weighted kernels Fisher discriminant analysis; classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Wen, T.; Yan, J.; Huang, D.; Lu, K.; Deng, C.; Zeng, T.; Yu, S.; He, Z. Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing. Sensors 2018, 18, 388.

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