PERTINENCE CURVES IN FUZZY MODELING OF THE PRODUCTIVE RESPONSES OF BROILERS

Detalhes bibliográficos
Autor(a) principal: Lourençoni,Dian
Data de Publicação: 2019
Outros Autores: Abreu,Paulo G. de, Yanagi Junior,Tadayuki, Campos,Alessandro T., Yanagi,Silvia de N. M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Engenharia Agrícola
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000300265
Resumo: ABSTRACT The selection of the type of fuzzy systems pertinence curve allows a better representation of the mathematical model and a smaller simulation error. We aimed to study the effect of pertinence curves in fuzzy modeling of broiler performance, created in different production systems. For the development and testing of fuzzy models, three commercial aviaries (conventional, tunnel with negative pressure, and dark house) were evaluated over one year, totaling six lots per system. For the development of the model, the input variables were enthalpy in each rearing phase (initial: phases 1, 2, and 3; growth: phase 4; and final: phase 5) and the output variables were feed intake (FE), weight gain (GP), feed conversion (FE), and the productive efficiency index (PEI). Triangular, trapezoidal, and Gaussian pertinence curves were combined and applied to represent the input and output fuzzy sets, totaling nine fuzzy models for each output variable. The combinations of pertinence curves provided adequate responses for the prediction of AL, GP, RC, and PEI. However, the selection of the types of curves should be studied on a case-by-case basis, so that the smallest possible simulation errors are obtained.
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spelling PERTINENCE CURVES IN FUZZY MODELING OF THE PRODUCTIVE RESPONSES OF BROILERSpoultry farmingproduction performanceartificial intelligencefuzzy logicABSTRACT The selection of the type of fuzzy systems pertinence curve allows a better representation of the mathematical model and a smaller simulation error. We aimed to study the effect of pertinence curves in fuzzy modeling of broiler performance, created in different production systems. For the development and testing of fuzzy models, three commercial aviaries (conventional, tunnel with negative pressure, and dark house) were evaluated over one year, totaling six lots per system. For the development of the model, the input variables were enthalpy in each rearing phase (initial: phases 1, 2, and 3; growth: phase 4; and final: phase 5) and the output variables were feed intake (FE), weight gain (GP), feed conversion (FE), and the productive efficiency index (PEI). Triangular, trapezoidal, and Gaussian pertinence curves were combined and applied to represent the input and output fuzzy sets, totaling nine fuzzy models for each output variable. The combinations of pertinence curves provided adequate responses for the prediction of AL, GP, RC, and PEI. However, the selection of the types of curves should be studied on a case-by-case basis, so that the smallest possible simulation errors are obtained.Associação Brasileira de Engenharia Agrícola2019-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000300265Engenharia Agrícola v.39 n.3 2019reponame:Engenharia Agrícolainstname:Associação Brasileira de Engenharia Agrícola (SBEA)instacron:SBEA10.1590/1809-4430-eng.agric.v39n3p265-271/2019info:eu-repo/semantics/openAccessLourençoni,DianAbreu,Paulo G. deYanagi Junior,TadayukiCampos,Alessandro T.Yanagi,Silvia de N. M.eng2019-06-17T00:00:00Zoai:scielo:S0100-69162019000300265Revistahttp://www.engenhariaagricola.org.br/ORGhttps://old.scielo.br/oai/scielo-oai.phprevistasbea@sbea.org.br||sbea@sbea.org.br1809-44300100-6916opendoar:2019-06-17T00:00Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)false
dc.title.none.fl_str_mv PERTINENCE CURVES IN FUZZY MODELING OF THE PRODUCTIVE RESPONSES OF BROILERS
title PERTINENCE CURVES IN FUZZY MODELING OF THE PRODUCTIVE RESPONSES OF BROILERS
spellingShingle PERTINENCE CURVES IN FUZZY MODELING OF THE PRODUCTIVE RESPONSES OF BROILERS
Lourençoni,Dian
poultry farming
production performance
artificial intelligence
fuzzy logic
title_short PERTINENCE CURVES IN FUZZY MODELING OF THE PRODUCTIVE RESPONSES OF BROILERS
title_full PERTINENCE CURVES IN FUZZY MODELING OF THE PRODUCTIVE RESPONSES OF BROILERS
title_fullStr PERTINENCE CURVES IN FUZZY MODELING OF THE PRODUCTIVE RESPONSES OF BROILERS
title_full_unstemmed PERTINENCE CURVES IN FUZZY MODELING OF THE PRODUCTIVE RESPONSES OF BROILERS
title_sort PERTINENCE CURVES IN FUZZY MODELING OF THE PRODUCTIVE RESPONSES OF BROILERS
author Lourençoni,Dian
author_facet Lourençoni,Dian
Abreu,Paulo G. de
Yanagi Junior,Tadayuki
Campos,Alessandro T.
Yanagi,Silvia de N. M.
author_role author
author2 Abreu,Paulo G. de
Yanagi Junior,Tadayuki
Campos,Alessandro T.
Yanagi,Silvia de N. M.
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Lourençoni,Dian
Abreu,Paulo G. de
Yanagi Junior,Tadayuki
Campos,Alessandro T.
Yanagi,Silvia de N. M.
dc.subject.por.fl_str_mv poultry farming
production performance
artificial intelligence
fuzzy logic
topic poultry farming
production performance
artificial intelligence
fuzzy logic
description ABSTRACT The selection of the type of fuzzy systems pertinence curve allows a better representation of the mathematical model and a smaller simulation error. We aimed to study the effect of pertinence curves in fuzzy modeling of broiler performance, created in different production systems. For the development and testing of fuzzy models, three commercial aviaries (conventional, tunnel with negative pressure, and dark house) were evaluated over one year, totaling six lots per system. For the development of the model, the input variables were enthalpy in each rearing phase (initial: phases 1, 2, and 3; growth: phase 4; and final: phase 5) and the output variables were feed intake (FE), weight gain (GP), feed conversion (FE), and the productive efficiency index (PEI). Triangular, trapezoidal, and Gaussian pertinence curves were combined and applied to represent the input and output fuzzy sets, totaling nine fuzzy models for each output variable. The combinations of pertinence curves provided adequate responses for the prediction of AL, GP, RC, and PEI. However, the selection of the types of curves should be studied on a case-by-case basis, so that the smallest possible simulation errors are obtained.
publishDate 2019
dc.date.none.fl_str_mv 2019-06-01
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=S0100-69162019000300265
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000300265
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1809-4430-eng.agric.v39n3p265-271/2019
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 Associação Brasileira de Engenharia Agrícola
publisher.none.fl_str_mv Associação Brasileira de Engenharia Agrícola
dc.source.none.fl_str_mv Engenharia Agrícola v.39 n.3 2019
reponame:Engenharia Agrícola
instname:Associação Brasileira de Engenharia Agrícola (SBEA)
instacron:SBEA
instname_str Associação Brasileira de Engenharia Agrícola (SBEA)
instacron_str SBEA
institution SBEA
reponame_str Engenharia Agrícola
collection Engenharia Agrícola
repository.name.fl_str_mv Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)
repository.mail.fl_str_mv revistasbea@sbea.org.br||sbea@sbea.org.br
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