Least square regression method for estimating gas concentration in an electronic nose system.

Sensors
Walaa KhalafManlio Gaudioso

Abstract

We describe an Electronic Nose (ENose) system which is able to identify the type of analyte and to estimate its concentration. The system consists of seven sensors, five of them being gas sensors (supplied with different heater voltage values), the remainder being a temperature and a humidity sensor, respectively. To identify a new analyte sample and then to estimate its concentration, we use both some machine learning techniques and the least square regression principle. In fact, we apply two different training models; the first one is based on the Support Vector Machine (SVM) approach and is aimed at teaching the system how to discriminate among different gases, while the second one uses the least squares regression approach to predict the concentration of each type of analyte.

References

Nov 2, 2004·Neural Computation·David R HardoonJohn Shawe-Taylor
Feb 5, 2008·IEEE Transactions on Neural Networks·K R MüllerB Schölkopf

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Software Mentioned

LIBSVM
DAQmx
LabWindows
CVI
NI

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