Classification and concentration prediction of combustible gas based on BPNN and PCA

DOI

10.2991/wartia-16.2016.315How to use a DOI?

Keywords

Back propagation neural network (BPNN), Principal component analysis (PCA), Concentration prediction, Classification of combustible gas.

Abstract

Detection of combustible gases is very important to reduce the modality and disability of human in both of civil and military situation. In this paper, a method of detection combustible gases of acetone and ethanol was proposed by using back propagation neural network (BPNN) and principal component analysis (PCA). The gas data were collected using some metal oxide semiconductor (MOS) gas sensors exposed to the mixture combustible gases of different concentration. The features of low and high frequency domain were extracted to establish a feature vector of 432 dimensions. Then PCA was used to reduce the dimension of feature vector from 432 to 11 which retained 99% information. The results showed the binary classification accuracy of BPNN is up to 100% for train, validation and test when distinguishing the combustible gas from the air. The mean and variance of error (0.004±0.008) for concentration prediction were obtained based on BPNN and PCA. The results demonstrated that the proposed method is effective for classification and concentration prediction of combustible gas.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hanguang Xiao
AU  - Junchan Xu
AU  - Bin Tang
AU  - Zhou Zhang
PY  - 2016/05
DA  - 2016/05
TI  - Classification and concentration prediction of combustible gas based on BPNN and PCA
BT  - Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications
PB  - Atlantis Press
SP  - 1561
EP  - 1568
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-16.2016.315
DO  - 10.2991/wartia-16.2016.315
ID  - Xiao2016/05
ER  -