| Yazarlar |
Suna Akkol
Yüzüncü Yıl Üniversitesi, Türkiye |
Dr. Öğr. Üyesi Aslı AKILLI
Ahi Evran Üniversitesi, Türkiye |
|
İbrahim Cemal
Adnan Menderes Üniversitesi, Türkiye |
| Özet |
| 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. |
| Anahtar Kelimeler |
| Artificial neural network | Hair goats | Live weight | Multiple linear regression | Prediction |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | Ulusal alan endekslerinde (TR Dizin, ULAKBİM) yayımlanan tam makale |
| Dergi Adı | Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi |
| Dergi ISSN | 1308-7576 |
| Dergi Tarandığı Indeksler | TR DİZİN |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2017 |
| Cilt No | 27 |
| Sayı | 1 |
| Sayfalar | 21 / 29 |
| Doi Numarası | 10.29133/yyutbd.263968 |
| Makale Linki | http://dergipark.gov.tr/yyutbd |
| Atıf Sayıları | |
| SCOPUS | 18 |
| Google Scholar | 31 |
