TY  - JOUR
T1  - A Comparison of Neural Network and Nonlinear Regression Predictions of Sheep Growth
AU - Behzadi, Mohammad Reza Bahreini AU - Aslaminejad, Ali Asghar 
JO  - Journal of Animal and Veterinary Advances
VL  - 9
IS  - 16
SP  - 2128
EP  - 2131
PY  - 2010
DA  - 2001/08/19
SN  - 1680-5593
DO  - javaa.2010.2128.2131
UR  - https://makhillpublications.co/view-article.php?doi=javaa.2010.2128.2131
KW  - Artificial neural network
KW  -nonlinear regression
KW  -nonlinear regression
KW  -nonlinear regression
KW  -biological process
KW  -Iran
AB  - This study evaluated the potential of Artificial Neural Networks (ANN) as an alternative to the traditional statistical regression techniques for the purpose of predicting Baluchi sheep growth. Weekly body weight data of 70 Baluchi lambs were recorded from birth to approximately 150th days of age. About 6 nonlinear regression forms of von Bertalanffy, Gompertz, Logistic (with 3 and 4 parameters) Brody and Richards were employed as counterparts to ANN. Goodness of fit and accuracy of the models were determined by coefficient of determination (R<SUP>2</SUP>), Mean Absolute Deviation (MAD), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE) and the bias. These forecasting error measurements are based on the difference between the estimated and observed values. ANN generated a slightly better descriptive sheep growth curve than the best one which generated from nonlinear models and made the most accurate prediction. It is concluded that ANN represents a valuable tool for predicting of lamb body weight.
ER  -