Modelling broilers' abdominal fat in response to dietary treatments

Neural networks are capable of modelling any complex function and can be used in poultry production. Dietary crude fibre (CF) and exogenous enzymes (exEn) extensively affected abdominal fat (AF) of broilers. Current methods to study AF and its correlation with dietary CF levels and exEn supplements are costly, laborious and time-consuming. The purpose of this study was to develop an artificial neural network-genetic algorithm (ANN-GA) to model data on the response of broiler chickens (AF) to CF and exEn from 0 to 42 days of age. A data set containing eight treatments was divided to the train, validation, and test data set of the ANN models. The information about feeding eight diets at two periods [starter (0-21 days of age) and grower (22-42 days of age)] were used to estimate AF of broilers by ANN-GA. A multilayer feed-forward neural network with different structures was developed using matlab software, and optimal values for the ANN weights were obtained using the genetic algorithm (GA). Crude fibre, and exEn were used as input variables and AF of broilers was output variable. The best model of ANN-GA was determined based on the train root mean square error (RMSE). The best selected ANN-GA showed desirable results, RMSE, 0.1286% and R(2) coefficient, 0.876 for test data.