Bulanık mantık yöntemi ve yapay sinir ağları kullanılarak farklı kokuların sınıflandırılması

Öz

Bu çalışmada 8 adet metal oksit gaz sensörü kullanılarak gerçekleştirilen bir elektronik burun devresi kullanılarak 9 farklı maddenin (yumurta, çürük yumurta, nane, naftalin, limon, melek otu kökü, aseton, oje ve gül suyu) ayırt edilmesi sağlanmıştır. Çalışmada veri sınıflandırma için bulanık mantık yöntemi ve yapay sinir ağları kullanılmıştır. Yapay sinir ağları kullanılarak gerçekleştirilen veri sınıflandırma işleminde farklı ağ mimarileri kullanılarak sınıflandırma performansları incelenmiştir. Bulanık mantık yöntemiyle gerçekleştirilen sınıflandırma işleminde ise kullanılan farklı üyelik fonksiyonları ile sınıflandırma doğruluğu artırılmaya çalışılmıştır. Yapay sinir ağları ile yapılan sınıflandırma işleminde gizli katman ve çıktı katmanında sırasıyla logaritmik sigmoid ve hiperbolik tanjant fonksiyonları kullanılarak %96.41 sınıflandırma doğruluğu elde edilmiştir. En yüksek sınıflandırma doğruluğunun elde edildiği bu yapay sinir ağının gizli katmanında 8 adet yapay sinir hücresi kullanılmıştır. Bulanık mantık yöntemi ile yapılan sınıflandırma işleminde ise çan üyelik fonksiyonunun kullanılmasıyla sınıflandırma doğruluğu %95.55 olarak elde edilmiştir.

Anahtar Kelimeler

• Elektronik burun
• Veri boyutu indirgeme
• Yapay sinir ağları
• Bulanık mantık yöntemi
• Veri sınıflandırma

Classification of different odors using fuzzy logic method and artificial neural network

Abstract

In this study, 9 different substances (egg, rotten egg, mint, naphthalene, lemon, angelica root, acetone, nail polish and rose water) were distinguished by using an electronic nose using 8 metal oxide gas sensors. In the study, fuzzy logic method and artificial neural networks were used for data classification. In the data classification process performed using artificial neural networks, the classification performances were examined using different network architectures. In the classification process carried out with the fuzzy logic method, an attempt was made to increase the classification accuracy with the different membership functions used. In the classification process performed with artificial neural networks, 96.41% classification accuracy was achieved by using logarithmic sigmoid and hyperbolic tangent functions in the hidden layer and output layer, respectively. 8 artificial nerve cells were used in the hidden layer of this artificial neural network, where the highest accuracy value was achieved. In the classification process carried out with the fuzzy logic method, the classification accuracy was achieved as 95.55% by using the bell membership function.

Keywords

• Electronic nose
• Data dimension reduction
• Artificial neural network
• Fuzzy logic method
• Data classification

Kaynakça

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