Egg hatchability prediction by multiple linear regression and artificial neural networks
An artificial neural network (ANN) was compared with a multiple linear regression statistical method to predict hatchability in an artificial incubation process. A feedforward neural network architecture was applied. Network trainings were made by the backpropagation algorithm based on data obtained...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Fundação APINCO de Ciência e Tecnologia Avícolas
2008-06-01
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| Series: | Brazilian Journal of Poultry Science |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2008000200004 |
| Summary: | An artificial neural network (ANN) was compared with a multiple linear regression statistical method to predict hatchability in an artificial incubation process. A feedforward neural network architecture was applied. Network trainings were made by the backpropagation algorithm based on data obtained from industrial incubations. The ANN model was chosen as it produced data that fit better the experimental data as compared to the multiple linear regression model, which used coefficients determined by minimum square method. The proposed simulation results of these approaches indicate that this ANN can be used for incubation performance prediction. |
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| ISSN: | 1516-635X 1806-9061 |