Egg hatchability prediction by multiple linear regression and artificial neural networks
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Poultry Science (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2008000200004 |
Resumo: | 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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Brazilian Journal of Poultry Science (Online) |
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Egg hatchability prediction by multiple linear regression and artificial neural networksArtificial incubationartificial neural networkshatchabilitymultiple linear regressionAn 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.Fundacao de Apoio a Ciência e Tecnologia Avicolas2008-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2008000200004Brazilian Journal of Poultry Science v.10 n.2 2008reponame:Brazilian Journal of Poultry Science (Online)instname:Fundação APINCO de Ciência e Tecnologia Avícolas (FACTA)instacron:FACTA10.1590/S1516-635X2008000200004info:eu-repo/semantics/openAccessBolzan,ACMachado,RAFPiaia,JCZeng2008-09-26T00:00:00Zoai:scielo:S1516-635X2008000200004Revistahttp://www.scielo.br/rbcahttps://old.scielo.br/oai/scielo-oai.php||rvfacta@terra.com.br1806-90611516-635Xopendoar:2008-09-26T00:00Brazilian Journal of Poultry Science (Online) - Fundação APINCO de Ciência e Tecnologia Avícolas (FACTA)false |
dc.title.none.fl_str_mv |
Egg hatchability prediction by multiple linear regression and artificial neural networks |
title |
Egg hatchability prediction by multiple linear regression and artificial neural networks |
spellingShingle |
Egg hatchability prediction by multiple linear regression and artificial neural networks Bolzan,AC Artificial incubation artificial neural networks hatchability multiple linear regression |
title_short |
Egg hatchability prediction by multiple linear regression and artificial neural networks |
title_full |
Egg hatchability prediction by multiple linear regression and artificial neural networks |
title_fullStr |
Egg hatchability prediction by multiple linear regression and artificial neural networks |
title_full_unstemmed |
Egg hatchability prediction by multiple linear regression and artificial neural networks |
title_sort |
Egg hatchability prediction by multiple linear regression and artificial neural networks |
author |
Bolzan,AC |
author_facet |
Bolzan,AC Machado,RAF Piaia,JCZ |
author_role |
author |
author2 |
Machado,RAF Piaia,JCZ |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Bolzan,AC Machado,RAF Piaia,JCZ |
dc.subject.por.fl_str_mv |
Artificial incubation artificial neural networks hatchability multiple linear regression |
topic |
Artificial incubation artificial neural networks hatchability multiple linear regression |
description |
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. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2008000200004 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2008000200004 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S1516-635X2008000200004 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Fundacao de Apoio a Ciência e Tecnologia Avicolas |
publisher.none.fl_str_mv |
Fundacao de Apoio a Ciência e Tecnologia Avicolas |
dc.source.none.fl_str_mv |
Brazilian Journal of Poultry Science v.10 n.2 2008 reponame:Brazilian Journal of Poultry Science (Online) instname:Fundação APINCO de Ciência e Tecnologia Avícolas (FACTA) instacron:FACTA |
instname_str |
Fundação APINCO de Ciência e Tecnologia Avícolas (FACTA) |
instacron_str |
FACTA |
institution |
FACTA |
reponame_str |
Brazilian Journal of Poultry Science (Online) |
collection |
Brazilian Journal of Poultry Science (Online) |
repository.name.fl_str_mv |
Brazilian Journal of Poultry Science (Online) - Fundação APINCO de Ciência e Tecnologia Avícolas (FACTA) |
repository.mail.fl_str_mv |
||rvfacta@terra.com.br |
_version_ |
1754122511407120384 |