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Evaluation of Normalization Techniques on Neural Networks for the Prediction of 305-Day Milk Yield    
Yazarlar (2)
Dr. Öğr. Üyesi Aslı AKILLI Dr. Öğr. Üyesi Aslı AKILLI
Kırşehir Ahi Evran Üniversitesi, Türkiye
Hülya Atıl
Ege Üniversitesi, Türkiye
Devamını Göster
Özet
In this study, the impact of data preprocessing on the prediction of 305-day milk yield using neural networks were investigated with regard to the effect of different normalization techniques. Eight normalization techniques “Z-Score, Min-Max, D-Min-Max, Median, Sigmoid, Decimal Scaling, Median and MAD, Tanh-Estimators" and five different back propagation algorithms “Levenberg-Marquardt (LM), Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG), Conjugate Gradient Back propagation with Powell-Beale Restarts (CGB) and Brayde Fletcher Gold Farlo Shanno Quasi Newton Back propagation (BFG)” were examined and tested comparatively for the analysis. Neural network architecture was optimized and tested with several experiments. Results of the analysis show that applying different normalization techniques affect the performance and the distribution of outputs influences the learning process of the neural network. The magnitude of the effects varied with the type of back propagation algorithms, activation functions, and network's architectural structure. According to the results of the analysis, the most successful performance value in the 305-day milk yield estimation was obtained by using the neural network structured by using the Decimal Scaling normalization technique with the Bayesian Regulation algorithm (R2Adj = 0.8181, RMSE= 0.0068, MAPE= 160.42 for test set; R2Adj =0.8141, RMSE= 0.0067, MAPE= 114.12 for validation set).
Anahtar Kelimeler
Makale Türü Özgün Makale
Makale Alt Türü Diğer hakemli uluslarası dergilerde yayınlanan tam makale
Dergi Adı Turkish Journal of Agricultural Engineering Research
Dergi ISSN 2717-8420
Dergi Tarandığı Indeksler CABI, Ebsco, Information Matrix for the Analysis of Journals (MIAR)
Makale Dili İngilizce
Basım Tarihi 09-2020
Cilt No 1
Sayı 2
Sayfalar 354 / 367
Doi Numarası 10.46592/turkager.2020.v01i02.011
Makale Linki https://dergipark.org.tr/tr/download/article-file/1201892