Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/33661
Título: Genetic fuzzy system for prediction of respiratory rate of chicks subject to thermal challenges
Título(s) alternativo(s): Sistema genético difuso para a predição da frequência respiratória de pintinhos sujeitos a desafios térmicos
Palavras-chave: Broiler
Computational intelligence
Physiological responses
Genetic algorithms
Frango de corte
Inteligência computacional
Respostas fisiológicas
Algorítmos genéticos
Data do documento: 2018
Editor: Departamento de Engenharia Agrícola - UFCG
Citação: FERRAZ, P. F. P. et al. Genetic fuzzy system for prediction of respiratory rate of chicks subject to thermal challenges. Revista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 22, n. 6, p. 412-417, 2018.
Resumo: The aim of this study was to estimate and compare the respiratory rate (breath min-1) of broiler chicks subjected to different heat intensities and exposure durations for the first week of life using a Fuzzy Inference System and a Genetic Fuzzy Rule Based System. The experiment was conducted in four environmentally controlled wind tunnels and using 210 chicks. The Fuzzy Inference System was structured based on two input variables: duration of thermal exposure (in days) and dry bulb temperature (°C), and the output variable was respiratory rate. The Genetic Fuzzy Rule Based System set the parameters of input and output variables of the Fuzzy Inference System model in order to increase the prediction accuracy of the respiratory rate values. The two systems (Fuzzy Inference System and Genetic Fuzzy Rule Based System) proved to be able to predict the respiratory rate of chicks. The Genetic Fuzzy Rule Based System interacted well with the Fuzzy Inference System model previously developed showing an improvement in the respiratory rate prediction accuracy. The Fuzzy Inference System had mean percentage error of 2.77, and for Fuzzy Inference System and Genetic Fuzzy Rule Based System it was 0.87, thus indicating an improvement in the accuracy of prediction of respiratory rate when using the tool of genetic algorithms.
URI: http://repositorio.ufla.br/jspui/handle/1/33661
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