A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems

Abstract

In this study, the feed forward neural networks (FFNNs) were applied and an adaptive neuro-fuzzy inference system (ANFIS) was proposed for quantitative identification of individual gas concentrations (trichloroethylene and acetone) in their gas mixtures. The quartz crystal microbalance (QCM) type sensors were used as gas sensors. The components in the binary mixture were quantified by applying the steady state sensor responses from the QCM sensor array as inputs to the FFNNs and ANFISs. The back propagation (BP) with momentum and adaptive learning rate algorithm, resilient BP (RP) algorithm, Fletcher–Reeves conjugate-gradient (CG) algorithm, Broyden, Fletcher, Goldfarb, and Shanno quasi-Newton (QN) algorithm, and Levenberg–Marquardt (LM) algorithm were performed as the training methods of the FFNNs. A hybrid training method, which was the combination of least-squares and back propagation algorithms, was used as the training method of the ANFISs. Quantitative analysis of trichloroethylene and acetone was evaluated in terms of training algorithms and methods.