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Sensors 2015, 15(7), 15198-15217; doi:10.3390/s150715198

A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance

1
College of Electronics and Information Engineering, Southwest University, Chongqing 400715, China
2
College of Communication Engineering, Chongqing University, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Academic Editor: W. Rudolf Seitz
Received: 3 April 2015 / Revised: 9 June 2015 / Accepted: 18 June 2015 / Published: 29 June 2015
(This article belongs to the Section Chemical Sensors)
View Full-Text   |   Download PDF [1560 KB, uploaded 29 June 2015]   |  

Abstract

In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC) has been proposed to analyze signals of an electronic nose (E-nose) used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO) algorithm is implemented in conjunction with support vector machine (SVM) for realizing a synchronization optimization of the sensor array and SVM model parameters. The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques. Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows. By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced. View Full-Text
Keywords: feature extraction; electronic nose; MWFC; QPSO; SVM feature extraction; electronic nose; MWFC; QPSO; SVM
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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MDPI and ACS Style

Guo, X.; Peng, C.; Zhang, S.; Yan, J.; Duan, S.; Wang, L.; Jia, P.; Tian, F. A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance. Sensors 2015, 15, 15198-15217.

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