Data/hora: 10/06/2024 05:32:01

Provedor de dados: 	86	
País: 	Brazil	
Título: 	Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs	
Autores: 	Mariano,Flávia Cristina Martins Queiroz; Lima,Renato Ribeiro de; Alvarenga,Renata Ribeiro; Rodrigues,Paulo Borges.	
Data: 	2020-01-01	
Ano: 	2020	
Palavras-chave: 	Broilers; Highest-probability density interval; Meta-analysis; Metabolizable energy; Percentage of success.	
Resumo: 	Abstract: The objective of this work was to compare the committee neural network (CNN) and weighted multiple linear regression (WMLR) models, in order to estimate the nitrogen-corrected apparent metabolizable energy (AMEn) of poultry feedstuffs. The prediction equation was adjusted by using a WMLR model and the meta-analysis principle. The models were compared by considering the correct prediction percentages, based on the classic prediction intervals and on the highest-probability density intervals, and by using a comparison test for proportions. The accuracy of the models was evaluated based on the values of the mean squared error, coefficient of determination, mean absolute deviation, mean absolute percentage error, and bias. Data from metabolic trials were used to compare the selected models. The committee neural network is the model that showed the highest accuracy of prediction, being recommended as the most accurate model to predict AMEn values for energetic concentrate feedstuffs used by the poultry feed industry.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2020000103600	
Editor: 	Embrapa Secretaria de Pesquisa e Desenvolvimento

Pesquisa Agropecuária Brasileira	
Relação: 	10.1590/s1678-3921.pab2020.v55.001199	
Formato: 	text/html	
Fonte: 	Pesquisa Agropecuária Brasileira  v.55 2020	
Fonte: 	info:eu-repo/semantics/openAccess