Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/42545
metadata.artigo.dc.title: Fuzzy-genetic approaches to knowledge discovery and decision making: Estimation of the cloacal temperature of chicks exposed to different thermal conditions
metadata.artigo.dc.creator: Hernández-Julio, Yamid F.
Ferraz, Patrícia F. P.
Yanagi Junior, Tadayuki
Ferraz, Gabriel A. e S.
Nieto-Bernal, Wilson
metadata.artigo.dc.subject: Fuzzy logic
Genetic algorithms
Computational intelligence
Physiological responses
Lógica Fuzzy
Algorítmos genéticos
Inteligência computacional
Respostas fisiológicas
metadata.artigo.dc.publisher: Elsevier
metadata.artigo.dc.date.issued: 25-Feb-2020
metadata.artigo.dc.identifier.citation: HERNÁNDEZ-JULIO, Y. F. et al. Fuzzy-genetic approaches to knowledge discovery and decision making: Estimation of the cloacal temperature of chicks exposed to different thermal conditions. Biosystems Engineering, London, 25 Feb. 2020. DOI: https://doi.org/10.1016/j.biosystemseng.2020.02.005.
metadata.artigo.dc.description.abstract: Behaviour and physiological responses (e.g. respiratory rate and cloacal temperature) could be an indication of the thermal comfort or discomfort of broilers chicks. This study aimed to estimate the cloacal temperature (CT) of chicks in response to different intensities and durations of thermal exposure during the first week of life using a fuzzy inference system (FIS) and a fuzzy genetic algorithm (Fuzzy-GA). The experiment was conducted in four temperature-controlled wind tunnels located at the environmental laboratory of the Federal University of Lavras (UFLA; Minas Gerais, Brazil). The experimental database is composed of 114 laboratory-based observations. The duration of thermal challenge (CD; days) and dry bulb temperature (tdb; °C) were used as input variables for FIS. This paper proposes a theoretical framework for the development of Fuzzy-GA systems via two different approaches: the Mogul approach and the Pittsburgh approach. According to our results, the predicted CT values for both models (FIS and Fuzzy-GA) were similar to the experimentally-observed CT values. However, we noted that the model based on Fuzzy-GA exhibited better statistical results than the manual FIS in terms of CT-predicting capability. Thus, the model based on Fuzzy-GA can be used to predict CT for chicks exposed to thermal challenges and can therefore aid in decision-making processes.
metadata.artigo.dc.identifier.uri: https://www.sciencedirect.com/science/article/abs/pii/S1537511020300453#!
http://repositorio.ufla.br/jspui/handle/1/42545
metadata.artigo.dc.language: en_US
Appears in Collections:DEA - Artigos publicados em periódicos
DEG - Artigos publicados em periódicos

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