02183naa a2200301 a 450000100080000000500110000800800410001902400640006010000250012424501400014926000090028950001600029852010320045865000180149065000250150865000200153365300130155365300270156665300410159365300560163465300180169065300260170865300260173470000190176070000210177970000210180077300600182121223262020-08-27       2020    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1590/S1678-3921. pab2020.v55.01199.2DOI1 aMARIANO, F. C. M. Q.  aCommittee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs.h[electronic resource]  c2020  aTítulo em português: Comitê de redes neurais e regressão múltipla ponderada para a predição de valores energéticos de alimentos para aves de corte.  aThe 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.  aMeta-analysis  aMetabolizable energy  aFrango de Corte  aBroilers  aEnergia metabolizável  aHighest-probability density interval  aIntervalo de credibilidade da máxima probabilidade  aMeta-análise  aPercentage of success  aPercentagem de acerto1 aLIMA, R. R. de1 aALVARENGA, R. R.1 aRODRIGUES, P. B.  tPesquisa Agropecuária Brasileiragv. 55, e01199, 2020.