期刊
SENSORS
卷 18, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/s18051446
关键词
gas sensor array; pattern recognition; artificial neural network; least squares; concentration estimation
资金
- Ministry of Trade, Industry and Energy (MOTIE, Korea) under the Technology Innovation Program [10073122]
Over the last few decades, the development of the electronic nose (E-nose) for detection and quantification of dangerous and odorless gases, such as methane (CH4) and carbon monoxide (CO), using an array of SnO2 gas sensors has attracted considerable attention. This paper addresses sensor cross sensitivity by developing a classifier and estimator using an artificial neural network (ANN) and least squares regression (LSR), respectively. Initially, the ANN was implemented using a feedforward pattern recognition algorithm to learn the collective behavior of an array as the signature of a particular gas. In the second phase, the classified gas was quantified by minimizing the mean square error using LSR. The combined approach produced 98.7% recognition probability, with 95.5 and 94.4% estimated gas concentration accuracies for CH4 and CO, respectively. The classifier and estimator parameters were deployed in a remote microcontroller for the actualization of a wireless E-nose system.
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