Using Empirical Modal Decomposition to Improve the Daily Milk Yield Prediction of Cows

Figure 1

Simulation architecture. The left architecture is a BPNN model fitted with the original daily milk yield series, and and represent the connection weights and bias between neurons in each BPNN model. The current daily milk yield enters the input layer of the BPNN model as an input value, and the output layer is the model predictor of the next day’s milk yield. The accuracy of the model prediction value is used as the criterion for evaluating the model. The right architecture is a BPNN model fitted with the denoised daily milk yield series.