GERİ BESLEMELİ VE İLERİ BESLEMELİ SİNİR AĞI KULLANILARAK DMMP VE CHCL3 GAZ KARIŞIMLARININ MİKTARSAL SINIFLANDIRILMASI
Öz
Bu çalışmada, gaz karışımlarında
gaz konsantrasyonlarının (DMMP and CHCl3) miktarsal olarak
tanımlanması için ileri beslemeli sinir ağı (FFNN) kullanılmış ve Elman geri
beslemeli sinir ağı (RNN) önerildi. Phthalocyanine kaplamalı kuartz kristal
mikrodenge (QCM) gaz sensörü olarak kullanıldı. Kalibreli bir kütle akış
kontrolörü, taşıyıcı gaz, DMMP ve CHCl3 gaz karışım buharlarının akış miktarının kontrol edilmesi
için kullanıldı. Algılayıcının cevapları
IEEE 488 kart vasıtasıyla toplandı. İkili karışımdaki bileşenler, QCM sensör
dizisinden alınan sensör cevapları ileri beslemeli sinir ağı ve Elman geri beslemeli
sinir ağına giriş olarak uygulanma ile
miktarsal olarak belirlendi. İki gizli
katmanlı Elman geri beslemeli sinir ağı ile en iyi sonuç elde edildi. DMMP and
CHCl3 gaz karışımlarının miktarsal sınıflandırılması için diğer
sinir ağlarının da uygulanabilirliği görüldü.
Anahtar Kelimeler
Gazların Miktarsal Sınıflandırılması İleri Beslemeli Sinir Ağı Elman Geri Beslemeli Sinir Ağı
Kaynakça
- D. Rebiere, C. Dejeoue, J. Pistre, R. Planade, J.F. Lipsker, P. Robin , Surface acustic wave detection of organophosphorus compounds with flouropolyol coatings, Sensors and actuators B, Vol. 43 (1997) 34-39
- M.S. Nieuwenhuizen, J.L.N. Harteveld, Studies on a surface acoustic wave (SAW) dosimeter sensor for organophosporous nerve agents, Sensors and actuators B, Vol. 40 (1997) 167-173
- M.H. Ho, G.G. Gullbault, B. Rietz, Continuos Detection of Toluene in Ambient Air with a Coated Piezoelectric Crystal, Anal. Chem., Vol. 52(9), (1980)
- F.Temurtas, C.Tasaltin, H.Temurtas, N. Yumusak, Z.Z: Ozturk, Fuzzy Logic and Neural Network Applicationns on the Gas Sensor Data: Concentration estimation, ISCIS’03, Antalya, LNCS (November 2003)178-185
- A. Szczurek, P.M. Szecowka, B.W. Licznerski, Application of sensor array and neural networks for quantification of organic solvent vapours in air, , Sensors and Actuators B, Vol. 58 (1999) 427-432
- S. Vaihinger, W. Gopel, Multi - Component Analysis in Chemical Sensing in Sensors: A Comprehensive Survery Ed. W. Gopel, S. Hense, S.N. Zemel, VCH. Weinhe, New York, Vol. 2(1) (1991) 192
- M. Pardo, G. Faglia, G. Sberveglieri, M. Corte, F. Masulli, M. Riani, A time delay neural network for estimation of gas concentrations in a mixture, Sensors and Actuators B, 65 (2000) 267–269
- H.W. King, Piezoelectric Sorption Detector, Anal. Chem., Vol. 36 (1964) 1735-1739
- J. Riddick, and A. Bunger, in A. Weissberger, (ed.), Organic Solvents’ in Techniques of Chemistry, Vol. 2, Wiley Interscience, (1970)
- M.M. Abdelhameed, F.F. Tolbah, A recurrent neural network based sequential controller for manufacturing automated systems, Vol. 12, (2002) 617-633
- S. Haykin, Neural Networks, A Comprehensive Foundation, Macmillan Publishing Company, Englewood Cliffs, N.J. (1994)
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
Quantitive Classification of Gases Feed Forward Neural Network Elman's Recurrent Neural Network
Kaynakça
- D. Rebiere, C. Dejeoue, J. Pistre, R. Planade, J.F. Lipsker, P. Robin , Surface acustic wave detection of organophosphorus compounds with flouropolyol coatings, Sensors and actuators B, Vol. 43 (1997) 34-39
- M.S. Nieuwenhuizen, J.L.N. Harteveld, Studies on a surface acoustic wave (SAW) dosimeter sensor for organophosporous nerve agents, Sensors and actuators B, Vol. 40 (1997) 167-173
- M.H. Ho, G.G. Gullbault, B. Rietz, Continuos Detection of Toluene in Ambient Air with a Coated Piezoelectric Crystal, Anal. Chem., Vol. 52(9), (1980)
- F.Temurtas, C.Tasaltin, H.Temurtas, N. Yumusak, Z.Z: Ozturk, Fuzzy Logic and Neural Network Applicationns on the Gas Sensor Data: Concentration estimation, ISCIS’03, Antalya, LNCS (November 2003)178-185
- A. Szczurek, P.M. Szecowka, B.W. Licznerski, Application of sensor array and neural networks for quantification of organic solvent vapours in air, , Sensors and Actuators B, Vol. 58 (1999) 427-432
- S. Vaihinger, W. Gopel, Multi - Component Analysis in Chemical Sensing in Sensors: A Comprehensive Survery Ed. W. Gopel, S. Hense, S.N. Zemel, VCH. Weinhe, New York, Vol. 2(1) (1991) 192
- M. Pardo, G. Faglia, G. Sberveglieri, M. Corte, F. Masulli, M. Riani, A time delay neural network for estimation of gas concentrations in a mixture, Sensors and Actuators B, 65 (2000) 267–269
- H.W. King, Piezoelectric Sorption Detector, Anal. Chem., Vol. 36 (1964) 1735-1739
- J. Riddick, and A. Bunger, in A. Weissberger, (ed.), Organic Solvents’ in Techniques of Chemistry, Vol. 2, Wiley Interscience, (1970)
- M.M. Abdelhameed, F.F. Tolbah, A recurrent neural network based sequential controller for manufacturing automated systems, Vol. 12, (2002) 617-633
- S. Haykin, Neural Networks, A Comprehensive Foundation, Macmillan Publishing Company, Englewood Cliffs, N.J. (1994)
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 16 Haziran 2004 |
| Yayımlandığı Sayı | Yıl 2004 Cilt: 2 Sayı: 3 |