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Sensors 2016, 16(4), 520; doi:10.3390/s16040520

Enhancing Electronic Nose Performance Based on a Novel QPSO-KELM Model

College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
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Author to whom correspondence should be addressed.
Academic Editors: Takeshi Onodera and Kiyoshi Toko
Received: 24 February 2016 / Revised: 27 March 2016 / Accepted: 7 April 2016 / Published: 11 April 2016
(This article belongs to the Special Issue Olfactory and Gustatory Sensors)
View Full-Text   |   Download PDF [1280 KB, uploaded 11 April 2016]   |  

Abstract

A novel multi-class classification method for bacteria detection termed quantum-behaved particle swarm optimization-based kernel extreme learning machine (QPSO-KELM) based on an electronic nose (E-nose) technology is proposed in this paper. Time and frequency domain features are extracted from E-nose signals used for detecting four different classes of wounds (uninfected and infected with Staphylococcu aureus, Escherichia coli and Pseudomonas aeruginosa) in this experiment. In addition, KELM is compared with five existing classification methods: Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), extreme learning machine (ELM), k-nearest neighbor (KNN) and support vector machine (SVM). Meanwhile, three traditional optimization methods including particle swarm optimization algorithm (PSO), genetic algorithm (GA) and grid search algorithm (GS) and four kernel functions (Gaussian kernel, linear kernel, polynomial kernel and wavelet kernel) for KELM are discussed in this experiment. Finally, the QPSO-KELM model is also used to deal with another two experimental E-nose datasets in the previous experiments. The experimental results demonstrate the superiority of QPSO-KELM in various E-nose applications. View Full-Text
Keywords: electronic nose; feature extraction; kernel extreme learning machine; quantum-behaved particle swarm optimization electronic nose; feature extraction; kernel extreme learning machine; quantum-behaved particle swarm optimization
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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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Peng, C.; Yan, J.; Duan, S.; Wang, L.; Jia, P.; Zhang, S. Enhancing Electronic Nose Performance Based on a Novel QPSO-KELM Model. Sensors 2016, 16, 520.

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