Table 5.

The comparison between nonlinear regression (NLR) models and artificial neural networks (ANN) models in the training phase^1,2

Items, MJ/d	n	RMSE		R 2		CCC
		NLR	ANN	NLR	ANN	NLR	ANN
ME intake	90	4.90	3.61	0.50	0.69	0.65	0.74
NE intake	90	3.74	2.94	0.49	0.74	0.66	0.81
NEm	45	1.23	0.70	0.64	0.86	0.78	0.90
NEp	90	0.61	0.41	0.29	0.72	0.46	0.79
NEl	45	1.45	0.36	0.88	0.97	0.94	0.98

¹The RMSE, R², and CCC were calculated using the predicted value and observed value in the whole training data set.

²CCC, concordance correlation coefficients; RMSE, root mean square error; ME, metabolic energy; NE, net energy; NE_m, NE for maintenance; NE_p, NE retained as protein; NE_l, NE retained as lipid.