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首页> 外文期刊>Neural computing & applications >Empirical comparisons of feed-forward connectionist and conventional regression models for prediction of first lactation 305-day milk yield in Karan Fries dairy cows
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Empirical comparisons of feed-forward connectionist and conventional regression models for prediction of first lactation 305-day milk yield in Karan Fries dairy cows

机译：前馈连接和传统回归模型在Karan Fries奶牛首次泌乳305天产奶量预测中的经验比较

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摘要

In this paper, two connectionist models are proposed based on different learning paradigms, viz., back propagation neural networks (BPNN) and radial basis function neural networks (RBFNN) to predict the first lactation 305-day milk yield (FLMY305) in Karan Fries (KF) dairy cattle. Also, a conventional multiple linear regression (MLR) model is developed for the prediction. In this study, all the models have been developed using a scientifically determined optimum dataset of representative breeding traits of the cattle. The prediction performances of the connectionist models are compared with that of the conventional model. This study shows that the RBFNN model performs relatively better than the MLR model. However, the BPNN model performs more or less in the close vicinity of the conventional MLR model. Hence, it is inferred that the connectionist models have potential as an alternative to the conventional models for predicting FLMY305 in KF cattle.
机译：本文基于不同的学习范式（即反向传播神经网络（BPNN）和径向基函数神经网络（RBFNN））提出了两种连接主义者模型，以预测Karan薯条的首次泌乳305天产奶量（FLMY305） （KF）奶牛。此外，开发了常规的多元线性回归（MLR）模型进行预测。在这项研究中，所有模型都是使用科学确定的代表牛典型繁殖性状的最佳数据集开发的。将连接模型的预测性能与常规模型的预测性能进行比较。这项研究表明，RBFNN模型的表现相对优于MLR模型。但是，BPNN模型在常规MLR模型的附近或多或少地执行。因此，可以推论，连接论者模型有可能替代传统模型来预测肯尼迪牛的FLMY305。

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