Kıl Keçilerinin Canlı Ağırlık Tahmininde Yapay Sinir Ağları ve Çoklu Doğrusal Regresyon Yöntemlerinin Karşılaştırılması

Öz

Yapay sinir ağları, insanlara benzer şekilde, örnekler üzerinden öğrenen yapay zeka temelli bir yöntemdir. Yapay sinir ağları yöntemi birçok farklı alanda olduğu gibi son yıllarda hayvancılık alanında da özellikle tahmin çalışmalarında regresyon analizine alternatif olarak sıklıkla kullanılmaktadır. Bu çalışmada 475 baş Kıl keçisine ilişkin morfolojik özellik ölçümlerinin canlı ağırlık üzerine etkileri yapay sinir ağları ve çoklu doğrusal regresyon analizi ile modellenmiş ve yöntemler bir karşılaştırmaya tabi tutulmuştur. Çalışmada yapay sinir ağları ile gerçekleştirilen analizlerde Levenberg-Marquart, Bayesian regularization and Scaled conjugate olmak üzere üç farklı geri yayılım algoritması kullanılmıştır. Yöntemlerin performansları düzeltilmiş belirleme katsayısı, hata kareler ortalamasının karekökü, ortalama mutlak sapma ve ortalama mutlak yüzde hata istatistikleri ile değerlendirilmiştir. Analiz sonucunda, Kıl keçilerinde canlı ağırlık tahmini bakımından yapay sinir ağlarının çoklu doğrusal regresyon analizine göre daha başarılı olduğu belirlenmiştir.

Anahtar Kelimeler

• Çoklu doğrusal regresyon analizi,Yapay sinir ağları,Kıl keçisi,Canlı ağırlık,Tahmin

Comparison of Artificial Neural Network and Multiple Linear Regression for Prediction of Live Weight in Hair Goats

Abstract

Artificial neural networks are artificial intelligence based methods which learns like humans, as humans did from instances. In recent years, artificial neural networks are often preferred in prediction studies of farm animals as like in many different fields as an alternative to regression analyses. In this study, based on measurements of morphologic traits of 475 Hair goats, the impact of different morphological measures on live weight has been modelled by artificial neural networks and multiple linear regression analyses. Comparison of these two models has been done. In the analyses done with the artificial neural networks method three different back propagation algorithms, such as Levenberg-Marquart, Bayesian regularization and Scaled conjugate, have been used. Methods performances have been determined with different criteria as coefficient of determination, mean absolute deviation, root mean square error and mean absolute percentage error. According to the analyses results, it’s noted that artificial neural networks method is more successful than multiple linear regression in prediction of body weight in hair goats.

Keywords

• Artificial neural network,Hair goats,Multiple linear regression,Live weight,Prediction

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