Gas identification based on committee machine for microelectronic gas sensor

Gas identification represents a big challenge for pattern recognition systems due to several particular problems such as nonselectivity and drift. The purpose of this paper is twofold: 1) to compare the accuracy of a range of advanced and classical pattern recognition algorithms for gas identification for the in-house sensor array signals and 2) to propose a gas identification ensemble machine (GIEM), which combines various gas identification algorithms, to obtain a unified decision with improved accuracy. An integrated sensor array has been designed with the aim of identifying combustion gases. The classification accuracy of different density models is compared with several neural network architectures. On the gas sensors data used in this paper, Gaussian mixture models achieved the best performance with higher than 94% accuracy. A committee machine is implemented by assembling the outputs of these gas identification algorithms through advanced voting machines using a weighting and classification confidence function. Experiments on real sensors' data proved the effectiveness of the system with an improved accuracy over the individual classifiers. An average performance of 97% was achieved using the proposed committee machine.