Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/48409 |
Resumo: | The objective of this work was to compare the committee neural network (CNN) and weighted multiple linear regression (WMLR) models, in order to estimate the nitrogen-corrected apparent metabolizable energy (AMEn) of poultry feedstuffs. The prediction equation was adjusted by using a WMLR model and the meta-analysis principle. The models were compared by considering the correct prediction percentages, based on the classic prediction intervals and on the highest-probability density intervals, and by using a comparison test for proportions. The accuracy of the models was evaluated based on the values of the mean squared error, coefficient of determination, mean absolute deviation, mean absolute percentage error, and bias. Data from metabolic trials were used to compare the selected models. The committee neural network is the model that showed the highest accuracy of prediction, being recommended as the most accurate model to predict AMEn values for energetic concentrate feedstuffs used by the poultry feed industry. |
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Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffsComitê de redes neurais e regressão múltipla ponderada para a predição de valores energéticos de alimentos para aves de corteCommittee neural networkWeighted multiple linear regressionBroilers - FeedstuffsHighest-probability density intervalMeta-analysisMetabolizable energyRegressão linear múltipla ponderadaFrangos de corte - DietaIntervalo de credibilidade da máxima probabilidadeMeta-análiseEnergia metabolizávelThe objective of this work was to compare the committee neural network (CNN) and weighted multiple linear regression (WMLR) models, in order to estimate the nitrogen-corrected apparent metabolizable energy (AMEn) of poultry feedstuffs. The prediction equation was adjusted by using a WMLR model and the meta-analysis principle. The models were compared by considering the correct prediction percentages, based on the classic prediction intervals and on the highest-probability density intervals, and by using a comparison test for proportions. The accuracy of the models was evaluated based on the values of the mean squared error, coefficient of determination, mean absolute deviation, mean absolute percentage error, and bias. Data from metabolic trials were used to compare the selected models. The committee neural network is the model that showed the highest accuracy of prediction, being recommended as the most accurate model to predict AMEn values for energetic concentrate feedstuffs used by the poultry feed industry.O objetivo deste trabalho foi comparar o modelo comitê de redes neurais e o modelo de regressão linear múltipla ponderada (RLMP), para estimar a energia metabolizável aparente corrigida por nitrogênio (EMAn) de alimentos para aves. A equação de predição foi ajustada por RLMP e pelo princípio da meta-análise. Os modelos foram comparados tendo-se considerando as percentagens de acerto de predição, com base em intervalos de predição clássicos e intervalos de credibilidade da máxima densidade de probabilidade, e utilizado um teste para comparação de proporções. A acurácia dos modelos foi avaliada com base nos valores de erro médio quadrático, coeficiente de determinação, desvio médio absoluto, erro percentual absoluto médio e viés. Dados provenientes de ensaios metabólicos foram utilizados na comparação dos modelos selecionados. O comitê de redes neurais é o modelo que forneceu predições mais acuradas, sendo recomendado como o de maior acurácia, para prever os valores de EMAn de alimentos concentrados utilizados na indústria alimentícia para aves.Embrapa Secretaria de Pesquisa e Desenvolvimento, Pesquisa Agropecuária Brasileira2021-10-26T17:59:21Z2021-10-26T17:59:21Z2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfMARIANO, F. C. M. Q. et al. Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs. Pesquisa Agropecuária Brasileira, Brasília, v. 55, e001199, 2020. DOI: 10.1590/S1678-3921.pab2020.v55.001199.http://repositorio.ufla.br/jspui/handle/1/48409Pesquisa Agropecuária Brasileirareponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessMariano, Flávia Cristina Martins QueirozLima, Renato Ribeiro deAlvarenga, Renata RibeiroRodrigues, Paulo Borgeseng2021-10-26T17:59:22Zoai:localhost:1/48409Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2021-10-26T17:59:22Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs Comitê de redes neurais e regressão múltipla ponderada para a predição de valores energéticos de alimentos para aves de corte |
title |
Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs |
spellingShingle |
Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs Mariano, Flávia Cristina Martins Queiroz Committee neural network Weighted multiple linear regression Broilers - Feedstuffs Highest-probability density interval Meta-analysis Metabolizable energy Regressão linear múltipla ponderada Frangos de corte - Dieta Intervalo de credibilidade da máxima probabilidade Meta-análise Energia metabolizável |
title_short |
Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs |
title_full |
Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs |
title_fullStr |
Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs |
title_full_unstemmed |
Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs |
title_sort |
Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs |
author |
Mariano, Flávia Cristina Martins Queiroz |
author_facet |
Mariano, Flávia Cristina Martins Queiroz Lima, Renato Ribeiro de Alvarenga, Renata Ribeiro Rodrigues, Paulo Borges |
author_role |
author |
author2 |
Lima, Renato Ribeiro de Alvarenga, Renata Ribeiro Rodrigues, Paulo Borges |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Mariano, Flávia Cristina Martins Queiroz Lima, Renato Ribeiro de Alvarenga, Renata Ribeiro Rodrigues, Paulo Borges |
dc.subject.por.fl_str_mv |
Committee neural network Weighted multiple linear regression Broilers - Feedstuffs Highest-probability density interval Meta-analysis Metabolizable energy Regressão linear múltipla ponderada Frangos de corte - Dieta Intervalo de credibilidade da máxima probabilidade Meta-análise Energia metabolizável |
topic |
Committee neural network Weighted multiple linear regression Broilers - Feedstuffs Highest-probability density interval Meta-analysis Metabolizable energy Regressão linear múltipla ponderada Frangos de corte - Dieta Intervalo de credibilidade da máxima probabilidade Meta-análise Energia metabolizável |
description |
The objective of this work was to compare the committee neural network (CNN) and weighted multiple linear regression (WMLR) models, in order to estimate the nitrogen-corrected apparent metabolizable energy (AMEn) of poultry feedstuffs. The prediction equation was adjusted by using a WMLR model and the meta-analysis principle. The models were compared by considering the correct prediction percentages, based on the classic prediction intervals and on the highest-probability density intervals, and by using a comparison test for proportions. The accuracy of the models was evaluated based on the values of the mean squared error, coefficient of determination, mean absolute deviation, mean absolute percentage error, and bias. Data from metabolic trials were used to compare the selected models. The committee neural network is the model that showed the highest accuracy of prediction, being recommended as the most accurate model to predict AMEn values for energetic concentrate feedstuffs used by the poultry feed industry. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 2021-10-26T17:59:21Z 2021-10-26T17:59:21Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
MARIANO, F. C. M. Q. et al. Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs. Pesquisa Agropecuária Brasileira, Brasília, v. 55, e001199, 2020. DOI: 10.1590/S1678-3921.pab2020.v55.001199. http://repositorio.ufla.br/jspui/handle/1/48409 |
identifier_str_mv |
MARIANO, F. C. M. Q. et al. Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs. Pesquisa Agropecuária Brasileira, Brasília, v. 55, e001199, 2020. DOI: 10.1590/S1678-3921.pab2020.v55.001199. |
url |
http://repositorio.ufla.br/jspui/handle/1/48409 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Embrapa Secretaria de Pesquisa e Desenvolvimento, Pesquisa Agropecuária Brasileira |
publisher.none.fl_str_mv |
Embrapa Secretaria de Pesquisa e Desenvolvimento, Pesquisa Agropecuária Brasileira |
dc.source.none.fl_str_mv |
Pesquisa Agropecuária Brasileira reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1807835065946734592 |