Journal
NEURAL COMPUTING & APPLICATIONS
Volume 15, Issue 3-4, Pages 359-365Publisher
SPRINGER
DOI: 10.1007/s00521-006-0037-y
Keywords
back-propagation networks; connectionist models; dairy production; Karan Fries cows; prediction; radial basis function networks; 305-day milk yield
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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.
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