comparison of the accuracy of nonlinear models and artificial neural network in the performance prediction of ross 308 broiler chickens

This study aimed to investigate and compare nonlinear growth models (nlms)with the predicted performance of broilers using an artificial neural network(ann). six hundred forty broiler chicks were sexed and randomly reared in 32separate pens as a factorial experiment with 4 treatments and 4 replicatesincluding 20 birds per pen in a 42-day period. treatments consisted of 2metabolic energy levels (3000 and 3100 kcal/kg), 2 crude protein levels (22and 24%) and two sexes. ten birds in each pen tagged and their weekly bwrecords were collected individually to evaluate the accuracy of predicted bwby ann as an alternative to nonlinear regression models (logistic, gompertz,von bertalanffy, and brody). based on the goodness of fit criteria and errormeasurement statistics, the nlms fitted the age-weight data better than ann.the findings indicated that the performance prediction of broiler chicks usingthe gompertz model (r^2 = 0.9989) was more accurate than other nlms (r^2 =0.9628 to 0.9988) and ann (r^2 = 0.95839). therefore, the application of thegompertz model is suggested to predict the bw changes of ross 308 broilerchicks over time.