Least Squares Neural Network-Based Wireless E-Nose System Using an SnO₂ Sensor Array.

ABSTRACT: 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% estimate

SUBMITTER: Shahid A 

PROVIDER: S-EPMC5982671 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature