Egg hatchability prediction by multiple linear regression and artificial neural networks

Detalhes bibliográficos
Autor(a) principal: Bolzan,AC
Data de Publicação: 2008
Outros Autores: Machado,RAF, Piaia,JCZ
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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spelling 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
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