Performance of fuzzy inference systems to predict the surface temperature of broiler chickens
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/33026 |
Resumo: | This study aimed to compare fuzzy systems with different configurations to predict the surface temperature (ts) of broiler chickens subjected to different intensities and durations of thermal challenges in the second week of life. Data on the ts of broiler chickens aged 8 to 11 days were acquired by infrared thermography and subjected to combinations of four dry-bulb temperatures (tdb) (24, 27, 30, and 33 °C) and four durations of thermal challenges (DTC) (1, 2, 3, or 4 days). The input variables of the fuzzy systems were tdb and DTC, and the output variable was ts. The Mamdani inference method involving five defuzzification methods [center of gravity (centroid), bisector of the area (bisector), largest of maximum (lom), middle of maximum (mom), and smallest of maximum (som)], and Sugeno inference with two defuzzification methods [weighted average (wtaver) and weighted sum (wtsum)] were evaluated. For both inference methods, triangular and Gaussian pertinence functions were tested for input and output variables, except for Sugeno inference, which used singletons functions as output variables. While developing fuzzy systems, different configurations must be compared, and the system with smaller simulation errors should be selected. |
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Performance of fuzzy inference systems to predict the surface temperature of broiler chickensPertinence functionsFuzzy logicDefuzzification methodsFuzzy inference methodsChicksInfrared thermographyThis study aimed to compare fuzzy systems with different configurations to predict the surface temperature (ts) of broiler chickens subjected to different intensities and durations of thermal challenges in the second week of life. Data on the ts of broiler chickens aged 8 to 11 days were acquired by infrared thermography and subjected to combinations of four dry-bulb temperatures (tdb) (24, 27, 30, and 33 °C) and four durations of thermal challenges (DTC) (1, 2, 3, or 4 days). The input variables of the fuzzy systems were tdb and DTC, and the output variable was ts. The Mamdani inference method involving five defuzzification methods [center of gravity (centroid), bisector of the area (bisector), largest of maximum (lom), middle of maximum (mom), and smallest of maximum (som)], and Sugeno inference with two defuzzification methods [weighted average (wtaver) and weighted sum (wtsum)] were evaluated. For both inference methods, triangular and Gaussian pertinence functions were tested for input and output variables, except for Sugeno inference, which used singletons functions as output variables. While developing fuzzy systems, different configurations must be compared, and the system with smaller simulation errors should be selected.Associação Brasileira de Engenharia Agrícola2019-02-25T13:49:43Z2019-02-25T13:49:43Z2018info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfBAHUTI, M. et al. Performance of fuzzy inference systems to predict the surface temperature of broiler chickens. Engenharia Agrícola, Jaboticabal, v. 38, n. 6, Nov./Dec. 2018.http://repositorio.ufla.br/jspui/handle/1/33026Engenharia Agrícolareponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessBahuti, MarceloAbreu, Lucas H. P.Yanagi Junior, TadayukiLima, Renato R. deCampos, Alessandro T.eng2019-02-25T13:50:07Zoai:localhost:1/33026Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2019-02-25T13:50:07Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Performance of fuzzy inference systems to predict the surface temperature of broiler chickens |
title |
Performance of fuzzy inference systems to predict the surface temperature of broiler chickens |
spellingShingle |
Performance of fuzzy inference systems to predict the surface temperature of broiler chickens Bahuti, Marcelo Pertinence functions Fuzzy logic Defuzzification methods Fuzzy inference methods Chicks Infrared thermography |
title_short |
Performance of fuzzy inference systems to predict the surface temperature of broiler chickens |
title_full |
Performance of fuzzy inference systems to predict the surface temperature of broiler chickens |
title_fullStr |
Performance of fuzzy inference systems to predict the surface temperature of broiler chickens |
title_full_unstemmed |
Performance of fuzzy inference systems to predict the surface temperature of broiler chickens |
title_sort |
Performance of fuzzy inference systems to predict the surface temperature of broiler chickens |
author |
Bahuti, Marcelo |
author_facet |
Bahuti, Marcelo Abreu, Lucas H. P. Yanagi Junior, Tadayuki Lima, Renato R. de Campos, Alessandro T. |
author_role |
author |
author2 |
Abreu, Lucas H. P. Yanagi Junior, Tadayuki Lima, Renato R. de Campos, Alessandro T. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Bahuti, Marcelo Abreu, Lucas H. P. Yanagi Junior, Tadayuki Lima, Renato R. de Campos, Alessandro T. |
dc.subject.por.fl_str_mv |
Pertinence functions Fuzzy logic Defuzzification methods Fuzzy inference methods Chicks Infrared thermography |
topic |
Pertinence functions Fuzzy logic Defuzzification methods Fuzzy inference methods Chicks Infrared thermography |
description |
This study aimed to compare fuzzy systems with different configurations to predict the surface temperature (ts) of broiler chickens subjected to different intensities and durations of thermal challenges in the second week of life. Data on the ts of broiler chickens aged 8 to 11 days were acquired by infrared thermography and subjected to combinations of four dry-bulb temperatures (tdb) (24, 27, 30, and 33 °C) and four durations of thermal challenges (DTC) (1, 2, 3, or 4 days). The input variables of the fuzzy systems were tdb and DTC, and the output variable was ts. The Mamdani inference method involving five defuzzification methods [center of gravity (centroid), bisector of the area (bisector), largest of maximum (lom), middle of maximum (mom), and smallest of maximum (som)], and Sugeno inference with two defuzzification methods [weighted average (wtaver) and weighted sum (wtsum)] were evaluated. For both inference methods, triangular and Gaussian pertinence functions were tested for input and output variables, except for Sugeno inference, which used singletons functions as output variables. While developing fuzzy systems, different configurations must be compared, and the system with smaller simulation errors should be selected. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018 2019-02-25T13:49:43Z 2019-02-25T13:49:43Z |
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 |
BAHUTI, M. et al. Performance of fuzzy inference systems to predict the surface temperature of broiler chickens. Engenharia Agrícola, Jaboticabal, v. 38, n. 6, Nov./Dec. 2018. http://repositorio.ufla.br/jspui/handle/1/33026 |
identifier_str_mv |
BAHUTI, M. et al. Performance of fuzzy inference systems to predict the surface temperature of broiler chickens. Engenharia Agrícola, Jaboticabal, v. 38, n. 6, Nov./Dec. 2018. |
url |
http://repositorio.ufla.br/jspui/handle/1/33026 |
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 |
Associação Brasileira de Engenharia Agrícola |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
dc.source.none.fl_str_mv |
Engenharia Agrícola 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_ |
1807835050585096192 |