TY  - JOUR
TT  - A Study on Binary Gas Mixture
AU  - Gülbağ, Ali
AU  - Kocamaz, Uğur Erkin
AU  - Uzun, Kader
PY  - 2005
DA  - August
JF  - Electronic Letters on Science and Engineering
PB  - Fevzullah TEMURTAŞ
WT  - DergiPark
SN  - 1305-8614
SP  - 7
EP  - 12
VL  - 1
IS  - 2
KW  - İkili gaz karışımı sınıflandırılması
KW  - yapay sinir ağları
N2  - In this study, quantitative classification of tricholoroethylene and carbontetrachloride was tried using steady state responses of sensors. For this purpose, Artificial Neural Networks (ANN) were used. ANN was used for gas concentration estimation and quantitative classification of the gas mixture. For gas concentration estimation, the gas sensor transient state responses were taken and for quantitative classification of the gas mixture, the gas sensor steady state responses were taken. A feed-forward multi-layer neural network with hidden layers trained by a backpropagation and Levenberg-Marquardt learning algorithms has been implemented. Acceptable performance is obtained for this system and the appropriateness of ANN for the quantitative classification of volatile organic compounds is observed.
UR  - https://dergipark.org.tr/en/pub/else/issue//313792
L1  - https://dergipark.org.tr/en/download/article-file/303922
ER  -