Data/hora: 20/05/2024 05:32:35

Provedor de dados: 	70	
País: 	Brazil	
Título: 	ARTIFICIAL NEURAL NETWORKS FOR PREDICTING ANIMAL THERMAL COMFORT	
Autores: 	Borges,Pedro H. M.; Mendoza,Zaíra M. S. H. de; Morais,Pedro H. M.; Santos,Ronei L. dos.	
Data: 	2018-12-01	
Ano: 	2018	
Palavras-chave: 	Time series; Artificial intelligence; Comfort index.	
Resumo: 	ABSTRACT The objective of this study was to develop artificial neural networks (ANNs) for predicting animal thermal comfort based on temperature and relative humidity of the air for each day of the year. The data on temperature and relative humidity for a 25-year historical series collected at the Padre Ricardo Remetter Conventional Meteorological Station, located in the city of Santo Antônio de Leverger - Mato Grosso (Brazil), were retrieved from the website of the National Institute of Meteorology. According to the day of the year, the temperature and humidity index was determined as a function of the climatic variables. Therefore, the day of the year was the input variable of the neural networks, and the temperature and humidity index (THI) was the output variable. The number of layers and neurons used for establishing different architectures was variable. Data were adjusted on the basis of mean square errors, performance and efficiency indexes, and normality tests. The values estimated by the networks and those obtained from the historical series did not differ significantly. The networks with the best performance were selected for graphical analysis of residuals. The ANNs developed in this study predicted animal thermal comfort with adequate reliability and precision.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162018000600844	
Editor: 	Associação Brasileira de Engenharia Agrícola	
Relação: 	10.1590/1809-4430-eng.agric.v38n6p844-856/2018	
Formato: 	text/html	
Fonte: 	Engenharia Agrícola  v.38 n.6 2018	
Fonte: 	info:eu-repo/semantics/openAccess