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

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

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 extract...

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Bibliographic Details
Main Authors: Tailai Wen, Jia Yan, Daoyu Huang, Kun Lu, Changjian Deng, Tanyue Zeng, Song Yu, Zhiyi He
Format: Article
Language:English
Published: MDPI AG 2018-01-01
Series:Sensors
Subjects:
electronic nose
feature extraction
multiple kernel learning
weighted kernels Fisher discriminant analysis
classification
Online Access:http://www.mdpi.com/1424-8220/18/2/388

Internet

http://www.mdpi.com/1424-8220/18/2/388

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