Predicting chick body mass by artificial intelligence-based models
Data(s) |
01/07/2014
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Resumo |
The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks) - with the variables dry-bulb air temperature, duration of thermal stress (days), chick age (days), and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro-fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems. |
Formato |
text/html |
Identificador |
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2014000700559 |
Idioma(s) |
en |
Publicador |
Embrapa Informação Tecnológica Pesquisa Agropecuária Brasileira |
Fonte |
Pesquisa Agropecuária Brasileira v.49 n.7 2014 |
Palavras-Chave | #animal welfare #artificial neural network #broiler #modeling #neuro-fuzzy network #thermal comfort |
Tipo |
journal article |