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

Tailai Wen
Tailai Wen
Daoyu Huang
Daoyu Huang
Changjian Deng
Changjian Deng
Tanyue Zeng
Tanyue Zeng
Zhiyi He
Zhiyi He

SENSORS, 2018.

Cited by: 4|Views2
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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...More

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