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首页> 外文期刊>Journal of Animal and Feed Sciences >Artificial NeuralNetwork for prediction of plasma hormones, liver enzymes and performance in broilers
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Artificial NeuralNetwork for prediction of plasma hormones, liver enzymes and performance in broilers

机译:人工神经网络可预测肉鸡血浆激素,肝酶和生产性能

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摘要

A three-layer feed forward Artificial Neural Network was trained to predict plasma hormones and liver enzymes in broiler chickens. Six diet parameters are selected as inputs (predictor) and eleven performances, plasma hormones or liver enzymes are chosen as output (predicted) parameters of the model. Birds were fed diets containing different concentration of energy and protein. A data set with 100 individuals is divided in two subsets (each contains 50 individuals) for training and evaluation of theneural network. A comparison is made between laboratory analysis results and the neural network predicted values for plasma hormones and liver enzymes. The neural network accurately forecasted the considered values in the first (training) subset and invalid answers were not identified in the second verification subset. The proposed model were found to most effective identify performance, plasma hormones or liver enzymes for broiler chickens fed the diets on the known range of the energy and protein concentration. Sensitivity analysis shows the dependency evaluation of the output parameters with respect to the inputs.
机译:训练了三层前馈人工神经网络来预测肉鸡的血浆激素和肝酶。选择六个饮食参数作为输入(预测值),选择十一种性能,血浆激素或肝酶作为模型的输出(预测)参数。给鸟类喂食含有不同浓度能量和蛋白质的饮食。具有100个人的数据集分为两个子集(每个子集包含50个人),用于训练和评估神经网络。将实验室分析结果与血浆激素和肝酶的神经网络预测值进行比较。神经网络准确地预测了第一个(训练)子集中的考虑值,并且在第二个验证子集中未识别出无效答案。发现该模型可以最有效地识别在能量和蛋白质浓度已知范围内饲喂日粮的肉鸡的性能,血浆激素或肝酶。灵敏度分析显示了输出参数相对于输入的依存性评估。

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