Norouzian Mohammad Ali, Bayatani Hossein, Vakili Alavijeh Mona
Department of Animal and Poultry Sciences, College of Abouraihan, University of Tehran, Tehran, Iran.
Department of Soft Computing, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.
Vet Res Forum. 2021 Winter;12(1):33-37. doi: 10.30466/vrf.2019.98275.2346. Epub 2021 Mar 15.
In this study, artificial neural networks (ANNs) were employed to investigate the relationship between locomotion score and production traits. A total number of 123 dairy cows from a free-stall housing farm were used in this study. To compare the effectiveness of the ANNs for the prediction of locomotion score, the multiple linear regression (MLR) model was developed using the eight production traits, body condition score, parity, days in milk, daily milk yield, milk fat percent, milk protein percent, daily milk fat yield, and daily milk protein yield as input variables to predict the locomotion score. The ANN predictions gave a higher coefficient of determination (R2) values with lower mean squared error (MSE) than MLR. The R2 and MSE of the MLR model were 0.53 and 0.36, respectively. However, the ANN model for the same dataset produced much improved results with R2 = 0.80 and MSE = 0.16, respectively. Globally, the results of this study showed that the connectionist network model was a better tool to predict locomotion scores compared to the multiple linear regression.
在本研究中，人工神经网络（ANNs）被用于研究运动评分与生产性状之间的关系。本研究使用了来自一个散栏式牛舍农场的总共123头奶牛。为了比较人工神经网络对运动评分预测的有效性，利用八个生产性状、体况评分、胎次、泌乳天数、日产奶量、乳脂率、乳蛋白率、日产乳脂量和日产乳蛋白量作为输入变量，建立了多元线性回归（MLR）模型来预测运动评分。与多元线性回归相比，人工神经网络预测得到的决定系数（R2）值更高，均方误差（MSE）更低。多元线性回归模型的R2和MSE分别为0.53和0.36。然而，针对同一数据集的人工神经网络模型产生了显著改善的结果，R2分别为0.80，MSE为0.16。总体而言，本研究结果表明，与多元线性回归相比，联结主义网络模型是预测运动评分的更好工具。
