Abstract
Comparıson And Predıctıon With Gompertz Growth Curve And Artıfıcal Neural Networks Models In Broıler Chıckens
Mathematical models offer great convenience in estimating the variation in the growth of the living. The time-dependent change in weight and body sizes of the living can be estimated easily via mathematical models. In this study, the growth curves of Ross 308 broilers were compared through the Gompertz model, which explains growth best and the ANN model, which is assumed to be an alternative to this model. The model with high-estimated R² and low-estimated MSE, MAD, and MAPE values were considered as the best model. The criteria obtained from the ANN and Gompertz models are 5625 and 2950 for MSE, 0.27, and 0.17 for MAPE; 0.5 and 1.2 for MAD, respectively while R² values were observed as 0.99 in both models. MSE and MAPE values were observed lower compared to the Gompertz model
Keywords
Broiler chickens, Artifical neural networks, Gompertz growth curve, Live weigh
