Beytullah EREN, Muhammad YAQUB, Volkan EYÜPOĞLU

Polimer içerikli membran verimi tahmininde yapay sinir ağları öğrenme algoritmalarının değerlendirilmesi

Bu çalışmanın amacı, polimer içerikli membranlar (PIMs) ile Cr (VI) giderimi için geliştirilecek yapay sinir ağı (YSA) modelinde optimum YSA mimarisi için en uygun öğrenme algoritmasının belirlenmesidir. Bu amaçla, geliştirilen yapay sinir ağı modelinde Levenberg-Marquardt, Bayesian Regularization, Ölçeklenmiş Konjuge Gradyan olmak üzere 3 faklı öğrenme algoritması uygulanmıştır. Ağ mimarisinin ve kullanılan öğrenme algoritmasının ağın tahmin performansına etkisinin belirlenmesinde Regresyon katsayısı (R2) ve ortalama karesel hata (OKH) teknikleri kullanılmıştır. Sonuç olarak geliştirilen bir YSA modelinde doğru öğrenme algoritması seçiminin ağın tahmin kabiliyeti açısından önemli olduğu sonucuna varılmıştır.

Anahtar Kelimeler:

yapay sinir ağları, öğrenme algoritması, levenberg-marquardt, bayesian regularization, ölçeklenmiş konjuge gradyen.

Assessment of neural network training algorithms for the prediction of polymeric inclusion membranes efficiency

The aim of this study is to introduce, through an appropriate selection of the training algorithm, a better and optimum artificial neural network (ANN) that will capable to predict Polymeric Inclusion Membranes (PIMs) Cr(VI) removal efficiency from aqueous solutions. To accomplish that, three training algorithms including Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) have been assessed by training different ANN. The performances of developed models are evaluated by Coefficient of Regression (R2) and Root Mean Square Error (RMSE) to find the best ANN training algorithms. This study clears that right choice of the training algorithm grants maximizing the predictive capability of the ANN models.

Keywords:

Neural Network, Training Algorithm, Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient.,

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