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Egg Production Curve Fitting Using Least Square Support Vector Machines and Nonlinear Regression Analysis      
Yazarlar
Prof. Dr. Özkan GÖRGÜLÜ
Ahi Evran Üniversitesi, Türkiye
Dr. Öğr. Üyesi Aslı AKILLI
Ahi Evran Üniversitesi, Türkiye
Özet
It was aimed to model egg production curves using nonlinear regression analysis and least squares support vector machines in this study. The accuracy of the models was calculated using the Akaike information criteria, mean square error, mean absolute percentage error, mean absolute deviation, R-2 and AdjR(2). The data set consisted of egg performance values of laying hens recorded from 20 weeks to 70 weeks of age. The longitudinal data had a nonlinear structure. The results showed that the least squares support vector machines method, which is considered in different parameter combinations, can be used as an alternative to classical methods and predictions have lower errors. The present study shows that least squares support vector machine methods can be used successfully in the modelling of egg production curves in laying hens.
Anahtar Kelimeler
egg production, last square support vector machine, curve fitting, regression, poultry
Makale Türü Özgün Makale
Makale Alt Türü SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale
Dergi Adı European Poultry Science
Dergi ISSN 1612-9199
Dergi Tarandığı Indeksler SCI-Expanded
Dergi Grubu Q3
Makale Dili İngilizce
Basım Tarihi 04-2018
Cilt No 82
Sayfalar 1 / 14
Doi Numarası 10.1399/eps.2018.235
BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
WoS 4
SCOPUS 4
Google Scholar 8
Egg Production Curve Fitting Using Least Square Support Vector Machines and Nonlinear Regression Analysis

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