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

Year 2017, Volume: 27 Issue: 1, 21 - 29, 31.03.2017

Suna Akkol , Aslı Akıllı İbrahim Cemal

https://doi.org/10.29133/yyutbd.263968

Cited By: 4

Abstract

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.

Keywords

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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