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Title: Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs
Other Titles: Comitê de redes neurais e regressão múltipla ponderada para a predição de valores energéticos de alimentos para aves de corte
Keywords: Committee neural network
Weighted multiple linear regression
Broilers - Feedstuffs
Highest-probability density interval
Metabolizable energy
Regressão linear múltipla ponderada
Frangos de corte - Dieta
Intervalo de credibilidade da máxima probabilidade
Energia metabolizável
Issue Date: 2020
Publisher: Embrapa Secretaria de Pesquisa e Desenvolvimento, Pesquisa Agropecuária Brasileira
Citation: MARIANO, F. C. M. Q. et al. Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs. Pesquisa Agropecuária Brasileira, Brasília, v. 55, e001199, 2020. DOI: 10.1590/S1678-3921.pab2020.v55.001199.
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.
Appears in Collections:DES - Artigos publicados em periódicos

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