Common Principal Component Analysis For Drift Compensation Of Gas Sensor Array Data

2009 ◽  
Author(s):  
A. Ziyatdinov ◽  
A. Chaudry ◽  
K. Persaud ◽  
P. Caminal ◽  
A. Perera ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Gas Sensor ◽  
Sensor Array ◽  
Principal Component ◽  
Component Analysis ◽  
Array Data ◽  
Gas Sensor Array ◽  
Drift Compensation

scholarly journals Drift compensation of gas sensor array data by common principal component analysis

2010 ◽  
Vol 146 (2) ◽  
pp. 460-465 ◽  
Author(s):  
A. Ziyatdinov ◽  
S. Marco ◽  
A. Chaudry ◽  
K. Persaud ◽  
P. Caminal ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Gas Sensor ◽  
Sensor Array ◽  
Principal Component ◽  
Component Analysis ◽  
Array Data ◽  
Gas Sensor Array ◽  
Drift Compensation

scholarly journals Drift compensation of gas sensor array data by Orthogonal Signal Correction

2010 ◽  
Vol 100 (1) ◽  
pp. 28-35 ◽  
Author(s):  
M. Padilla ◽  
A. Perera ◽  
I. Montoliu ◽  
A. Chaudry ◽  
K. Persaud ◽  
...  
Keyword(s):  
Gas Sensor ◽  
Sensor Array ◽  
Orthogonal Signal ◽  
Array Data ◽  
Gas Sensor Array ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Drift Compensation

Gas Mixture Recognition Method with New Hybrid Architecture

2010 ◽  
Vol 40-41 ◽  
pp. 604-609
Author(s):  
Shuang Yan Zhang ◽  
Jun Yu ◽  
Guang Fen Wei ◽  
Zhen An Tang ◽  
Yi Chen ◽  
...  
Keyword(s):  
Gas Sensor ◽  
Sensor Array ◽  
Array Signal Processing ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Gas Mixture ◽  
Hybrid Architecture ◽  
Gas Sensor Array ◽  
Binary Gas Mixture

The quantification accuracy of the gas mixture recognizing is greatly dependent on the gas sensor array signal processing method. The paper reports the new hybrid architecture with two main stages for gas mixture recognition. The first stage combine the principal component analysis (PCA) and back propagation neural network (BPNN) to qualitative identify the gas mixture, and the second stage composed of the independent component analysis (ICA) and BP sub networks to quantify the gas concentrations. The hybrid architecture and three other commonly used methods of PCA+BPNN, ICA+BPNN, and ICA+BP sub networks were respectively applied in binary gas mixture quantification based on the same gas sensor array, and results show that the hybrid architecture has the lowest quantitative recognition errors and fast converge speed comparing with the other methods.

Sign in / Sign up

Export Citation Format

Share Document