TY - JOUR
T1 - Neural, fuzzy and neuro-fuzzy approach for concentration estimation of volatile organic compounds by surface acoustic wave sensor array
AU - Jha, Sunil K.
AU - Hayashi, Kenshi
AU - Yadava, R. D.S.
N1 - Funding Information:
This work was supported in part by the Defence Research & Development Organization, (DRDO), Government of India, under Grant ERIP-ER-0703643-01-1025 and Grant-in-Aid for JSPS Fellows 24.02367 from Japan Society for the Promotion of Science (JSPS). The author S.K.J. gratefully acknowledges all the lab colleagues and Mrs. Anju Sunil Jha for their cooperation and support during the study. We are also thankful to the authors whose published experimental data sets are used in this study and reviewers for their valuable suggestion.
PY - 2014/9
Y1 - 2014/9
N2 - 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.
AB - 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.
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U2 - 10.1016/j.measurement.2014.05.002
DO - 10.1016/j.measurement.2014.05.002
M3 - Article
AN - SCOPUS:84901669586
SN - 0263-2241
VL - 55
SP - 186
EP - 195
JO - Measurement: Journal of the International Measurement Confederation
JF - Measurement: Journal of the International Measurement Confederation
ER -