Neural, fuzzy and neuro-fuzzy approach for concentration estimation of volatile organic compounds by surface acoustic wave sensor array

Present study evaluates application of adaptive neuro-fuzzy inference system (ANFIS) for concentration estimation of volatile organic compounds (VOCs) by analyzing response matrix of polymer-functionalized surface acoustic wave (SAW) sensor array. The performance of ANFIS is compared with that of subtractive clustering based fuzzy inference system (SC-FIS) and backpropagation artificial neural network (BP-ANN). For analysis, the raw SAW sensor array data is preprocessed by logarithmic scaling followed by dimensional autoscaling and the feature extraction by principal component analysis (PCA). For concentration prediction, the extracted feature vectors were fed as input to the three methods (ANFIS, SC-FIS and BP-ANN) independently. The performance of the three methods were evaluated on the basis of root mean square error (RMSE) and correlation value involving actual and estimated values of concentration. Five sets of SAW sensor array responses are analyzed. The analysis includes both experimental and synthetic (sensor model generated) data sets. It is found that the ANFIS has the least value of RMSE and highest value of correlation compared to SC-FIS and BP-ANN. This signifies the relative superiority of ANFIS method.