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

In this study, the daily lactation data of Holstein dairy cows in one lactation period (305 days) were used as lactation time series data. Empirical mode decomposition (EMD) was used to decompose milk yield series. The nonstationary milk yield series with multiple oscillation modes was decomposed in...

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Bibliographic Details
Published in:Wireless communications and mobile computing 2022-07, Vol.2022, p.1-7
Main Authors: Cao, Zhiyong, Cao, Zhijuan, Zhao, Hongwei, Xu, Jiajun, Zhang, Guangyong, Li, Yi, Su, Yufei, Lou, Ling, Yang, Xiujuan, Gu, Zhaobing
Format: Article
Language:English
Subjects:
Animal lactation
Artificial neural networks
Back propagation networks
Bias
Dairy cattle
Goats
Interference
Mathematical models
Milk
Neural networks
Noise reduction
Oscillation modes
Prediction models
Simulation
Trends
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Summary:In this study, the daily lactation data of Holstein dairy cows in one lactation period (305 days) were used as lactation time series data. Empirical mode decomposition (EMD) was used to decompose milk yield series. The nonstationary milk yield series with multiple oscillation modes was decomposed into several components. After eliminating the interference components, the interference components were superimposed. Remaining component reconstruction was used to get the denoising milk yield series. The denoising milk yield series retained the basic characteristics of the original milk yield series and corrected the errors of the original data. The back propagation neural network (BPNN) was used to predict and compare the original milk yield series and the denoising milk yield series. The results showed that it was feasible to use EMD to smooth the original daily milk production data. The noise reduction milk production series was beneficial to the learning of prediction model and could improve the accuracy of prediction of daily milk production of dairy cows.
ISSN:1530-8669
1530-8677
DOI:10.1155/2022/1685841