Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/45233
標題: Comparison of Regression and Artificial Neural Network Models of Egg Production
作者: B.Y.Wang
S.A.Chen
S.W.Roan
摘要: This study compared the relationship between egg production and the number of pullets, laying hens, culling birds and molting birds in Taiwan through Traditional Regression Methods and ANN (Artificial Neural Network) Models. Egg production data and the number of laying hens associated with each data set were gathered from the National Animal Industry Foundation for dates between January 2001 and March 2011, totalling 123 data sets. The final regression equations were: Traditional Regression Model: case = 2.77 + 0.696 Pmonth – 0.00621 Pmonth2 – 0.00163 pullet + 0.0025 laying, R2 = 0.699; ANN Model: case = 2.82+0.113 Pmonth – 0.00871 Pmonth2 – 0.00157 pullet + 0.0024 laying, R2 = 0.965. These results show that the ANN Method is more accurate than traditional Regression Models for predicting egg production in Taiwan.
URI: http://hdl.handle.net/11455/45233
文章連結: http://dx.doi.org/10.3923/javaa.2012.2503.2508
Appears in Collections:動物科學系

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