Predicting chick body mass by artificial intelligence-based models
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.
| Principais autores: | , , , , , , |
|---|---|
| Formato: | Digital revista |
| Idioma: | English |
| Publicado em: |
Embrapa Secretaria de Pesquisa e Desenvolvimento
2014
|
| Acesso em linha: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2014000700559 |
| Tags: |
Adicionar Tag
Sem tags, seja o primeiro a adicionar uma tag!
|