Yapay Sinir Ağları ile Yumurta Veriminin Tahmini

Sinir ağı, insan beyninin çalışmasına dayanan bilgi işlemenin matematiksel bir modeldir. Yapay sinir ağı (YSA), bir ağda birbirine bağlanan bir takım basit işlem öğelerinden oluşur. Bu çalışmada, yapay sinir ağı modeli kullanılarak yumurtacılarda bireysel olarak toplanan kuluçka dönemi, sıra, canlı ağırlık, eşeysel olgunluğa ulaşma yaşı ve eşeysel olgunluk ağırlığı ölçümlerine dayanılarak yumurta verimi tahminlenmiştir. İleri geribildirim algoritması tarafından eğitilen çok tabakalı yapay sinir ağı modeli, ileri beslemeli sinir ağı öğrenmesi için kullanılmıştır. Mevcut veri seti eğitim ve test seti olmak üzere iki kısma ayrılmıştır. Modelin iyi uyumunun belirlenmesinde belirleme katsayısı (R2 ), hata kareler ortalamasının karekökü (RMSE) ve ortalama mutlak sapma (MAD) kriterleri kullanılmıştır. Eğitim ve test seti için R2 değeri sırasıyla 0.80 ve 0.82 olarak tahminlenmiştir. Çalışmada düşük RMSE ve MAD değerleri elde edilmiştir. Uygulama sonucuna göre yumurta üretiminin tahmininde yapay sinir ağı kullanılabileceği belirlenmiştir.

Use of Neural Network Model to Predict of Egg Yield

A neural network is a mathematical model of information processing based on the work of the humanbrain. An artificial neural network (ANN) is composed of a number of simple processing elements connected togetherin a network. In this study, the egg yield was predicted based on the individually collected hatching period, line,body weight (BW), age at sexual maturity (ASM) and body weight at sexual maturity (BWSM) records of layersusing neural network model. A multilayer perceptron (MLP) ANN model trained by back propagation algorithm isdeveloped for feed-forward neural network learning. From the available data set, training and testing sets wereextracted. Goodness of fit of the model was determined with the coefficient of determination (R2), root mean squareerror (RMSE) and Mean Absolute Deviation (MAD) values. The R2 for training and test sets were estimated to be0.80 and 0.82, respectively. Lower RMSE and MAD values were obtained. The empirical result shows that neuralnetwork can be used for the prediction of egg yield.

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Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi-Cover

ISSN: 1300-2910
Yayın Aralığı: Yılda 3 Sayı
Başlangıç: 1985
Yayıncı: Tokat Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi Yayın Ofisi