@ARTICLE{Lentka_Łukasz_Determination_2015, author={Lentka, Łukasz and Smulko, Janusz M. and Ionescu, Radu and Granqvist, Claes G. and Kish, Laszlo B.}, volume={vol. 22}, number={No 3}, journal={Metrology and Measurement Systems}, pages={341-350}, howpublished={online}, year={2015}, publisher={Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation}, abstract={This paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based) nonlinear regression to determine the gas concentration of each constituent in a mixture. We confirmed that the accuracy of the estimated gas concentration could be significantly improved by applying temperature change and ultraviolet irradiation of the WO3 layer. Fluctuation-enhanced sensing allowed us to predict the concentration of both component gases.}, type={Artykuły / Articles}, title={Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm}, URL={http://journals.pan.pl/Content/90351/PDF/Journal10178-Volume%20XXII%20Issue3_02paper.pdf}, doi={10.1515/mms-2015-0039}, keywords={LS-SVM algorithm, resistance gas sensor, fluctuation enhanced sensing, gas detection}, }