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首页> 外文期刊>Current Science: A Fortnightly Journal of Research >Comparative study of feed-forward neuro-computing with multiple linear regression model for milk yield prediction in dairy cattle
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Comparative study of feed-forward neuro-computing with multiple linear regression model for milk yield prediction in dairy cattle

机译：前馈神经计算与多元线性回归模型预测奶牛产奶量的比较研究

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摘要

The main objective of this work is to compare the accuracy of artificial neural networks (ANNs) and multiple linear regression (MLR) model for prediction of first lactation 305-day milk yield (FL305DMY) using monthly test-day milk yield records of 443 Frieswal cows. We have compared four versions of feed-forward algorithm with conventional statistical model. The performancre of ANN is found to be better than the MLR model for milk yield prediction. The Bayesian regularization neural network model was able to predict milk yield with 85.07% accuracy as early as 126th day of lactation. It has been found that R-2 value of the models increases with increase in the number of test-day milk yield records.
机译：这项工作的主要目的是使用443 Frieswal的每月测试日牛奶产量记录，比较人工神经网络（ANN）和多元线性回归（MLR）模型预测第一次泌乳305天牛奶产量（FL305DMY）的准确性。牛。我们将四种版本的前馈算法与常规统计模型进行了比较。发现ANN的性能要比MLR模型更好地预测牛奶产量。贝叶斯正则化神经网络模型能够在泌乳第126天时以85.07％的准确度预测牛奶产量。已经发现，模型的R-2值随着测试日产奶量记录的增加而增加。

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