Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/41019
metadata.artigo.dc.title: Productive responses from broiler chickens raised in different commercial production systems - part I: fuzzy modeling
metadata.artigo.dc.creator: Lourençoni, Dian
Yanagi Junior, Tadayuki
Abreu, Paulo G. de
Campos, Alessandro T.
Yanagi, Silvia de N. M.
metadata.artigo.dc.subject: Poultry farming
Productive performance
Artificial intelligence
Fuzzy logic
metadata.artigo.dc.publisher: Associação Brasileira de Engenharia Agrícola
metadata.artigo.dc.date.issued: 2019
metadata.artigo.dc.identifier.citation: LOURENÇONI, D. et al. Productive responses from broiler chickens raised in different commercial production systems - part I: fuzzy modeling. Engenharia Agrícola, Jaboticabal, v. 39, n. 1, p. 1-10, Jan./Feb. 2019.
metadata.artigo.dc.description.abstract: Broiler chickens are classified as homoeothermic animals and require a production environment within well-defined thermal comfort intervals. Therefore, the development of algorithms (mathematical models) to control the environment that can be embedded in microcontrollers becomes necessary. Hence, this work aimed to develop a fuzzy model for predicting the productive performance of broiler chickens as a function of the thermal environment during the various breeding phases. The Mamdani inference and defuzzification methods were used, by means of the gravity center, to develop the fuzzy model. Two hundred and forty-three rules with weighting factors of 1.0 each were elaborated. Three commercial warehouses (conventional system, wind tunnel with negative pressure and dark house) were evaluated for testing of the model. We recorded the thermal environment (dry bulb temperature - tdb and relative humidity - RH) and productivity data (feed intake - FI, weight gain - WG, feed conversion - FC and productive efficiency index - PEI) over six lots in each aviary. The resulting fuzzy model was capable of forecasting FI, WG, FC, and PEI, with standard deviations and mean percentage errors of 4.16 g and 5.05%, 146.53 g and 8.04%, 0.06 g g-1 and 4.96%, and 24.51 g and 12.29%, respectively.
metadata.artigo.dc.identifier.uri: http://repositorio.ufla.br/jspui/handle/1/41019
metadata.artigo.dc.language: en_US
Appears in Collections:DEG - Artigos publicados em periódicos
DRS - Artigos publicados em periódicos



This item is licensed under a Creative Commons License Creative Commons