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document
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 .
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Intervention_Physical
Dietary crude fibre
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Intervention_Pharmacological
( CF )
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Intervention_Physical
exogenous enzymes
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Intervention_Pharmacological
( exEn )
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Intervention_Physical
CF
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Intervention_Physical
exEn
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Outcome_Mental
response of broiler chickens ( AF )