Please use this identifier to cite or link to this item:
|Title:||Models for prediction of physiological responses of Holstein dairy cows|
|Keywords:||Artificial neural networks|
Holstein dairy cows
|Publisher:||Taylor & Francis|
|Citation:||HERNÁNDEZ-JULIO, Y. F. et al. Models for prediction of physiological responses of Holstein dairy cows. Applied Artificial Intelligence, [S.l.], v. 28, n. 8, p. 766-792, Oct. 2014. DOI: 10.1080/08839514.2014.952919.|
|Abstract:||The goal of the present study was to evaluate techniques for modeling the physiological responses, rectal temperature, and respiratory rate of black and white Holstein dairy cows. Data from the literature (792 data points) and obtained experimentally (5884 data points) were used to fit and validate the models. Each datum included dry bulb air temperature, relative humidity, rectal temperature, and respiratory rate. Two models based on artificial intelligence-artificial neural networks and neurofuzzy networks-and one based on regression were evaluated for each response variable. The adjusted models predict rectal temperature and respiratory rate as a function of dry-bulb air temperature and relative humidity. These models were compared using statistical indices. The model based on artificial neural networks showed the best performance, followed by the models based on neurofuzzy networks and regression; the last two performed similarly.|
|Appears in Collections:||DEG - Artigos publicados em periódicos|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.