Comparative study of feed-forward neuro-computing with multiple linear regression model for milk yield prediction in dairy cattle

被引：0

作者：

Bhosale, Manisha Dinesh ^{[1
]}

Singh, T. P. ^{[1
]}

机构：

[1] Symbiosis Int Univ, Symbiosis Inst Geoinformat, Pune 411016, Maharashtra, India

来源：

CURRENT SCIENCE | 2015年 / 108卷 / 12期

关键词：

Artificial neural network; dairy cattle; milk yielded; multiple linear regression; ARTIFICIAL NEURAL-NETWORKS; SAHIWAL CATTLE; LACTATION; COWS;

D O I：

暂无

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

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.

引用

页码：2257 / 2261

页数：5

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