Gas identification algorithms for microelectronic gas sensor

Gas identification represents a big challenge for pattern recognition systems due to several particular problems. The aim of this study is to compare the accuracy of a range of advanced and classical pattern recognition algorithms for gas identification from sensor array signals. Density estimation is applied in the construction of classifiers through the use of Bayes rule. Experiments on real sensor´s data proved the effectiveness of the approach with an excellent classification performance. We compare the classification accuracy of different density models with several neural networks architectures. On our gas sensors data, the best performance was achieved by Gaussian mixture models with more than 92% accuracy.