Signal Processing for Multi-sensor E-nose System: Acquisition and Classification

被引：0

作者：

Rahman, Md. Mizanur ^{[1
,2
]}

Charoenlarpnopparut, Chalie ^{[1
]}

Suksompong, Prapun ^{[1
]}

机构：

[1] Thammasat Univ, Elect & Commun Engn, SIIT, Pathum Thani, Thailand

[2] Khulna Univ, Elect & Commun Engn, Khulna, Bangladesh

来源：

2015 10TH INTERNATIONAL CONFERENCE ON INFORMATION, COMMUNICATIONS AND SIGNAL PROCESSING (ICICS) | 2015年

关键词：

LDA; FFBPNN; SVM; RBF; E-Nose; sensors; ELECTRONIC NOSE; SENSORS;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper we review principle component analysis, linear discriminant analysis (LDA), k-nearest neighbor, feed forward backpropagation neural network, support vector machine, and radial basis function neural network (RBFNN) algorithms applied to electronic nose (E-Nose) for classification and detection. We show a method to extend the linear discriminant analysis (LDA) for multiclass (i.e. more than two class) LDA. By considering data alike typical E-Nose response we also show that RBFNN method need less time to classify new data. Thus RBFNN is more prominent in real time application for object identification from odor.

引用

页数：5

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