QUANTITATIVE CLASSIFICATION OF DMMP AND CHCL3 GAS MİXTURES USING RECURRENT AND FEED FORWARD NEURAL NETWORKS
Öz
In this study, the feed forward neural networks
(FFNN) were used and Elman’s recurrent neural networks (RNN) were proposed for
quantitative identification of individual gas concentrations (DMMP and CHCl3)
in their gas mixtures. The phthalocyanine coated quartz crystal microbalance
(QCM) type sensors were used as gas sensors. A calibrated mass flow controller
was used to control the flow rates of carrier gas and DMMP and CHCl3
gas mixtures streams. Sensor responses were collected via an IEEE 488 card. The
components in the binary mixture were quantified applying the sensor responses
from the QCM sensor array as inputs to the feed forward and Elman’s recurrent
neural networks. The results of the Elman’s recurrent neural network with two
hidden layer was the best. The other neural networks are also applicable to the
quantitative classification of DMMP and CHCl3 gas mixtures.
Anahtar Kelimeler
Kaynakça
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Ayrıntılar
Birincil Dil
İngilizce
Konular
-
Bölüm
Araştırma Makalesi
Yayımlanma Tarihi
16 Haziran 2004
Gönderilme Tarihi
2 Aralık 2003
Kabul Tarihi
-
Yayımlandığı Sayı
Yıl 2004 Cilt: 2 Sayı: 3