Skip to main content

Table 5 The average of fitting criteria of RMSE, RMSE%, AIC, BIC, FI and AAE according to the number of hidden layers

From: Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height

The alternatives fort the number of hidden layes

RMSE

RMSE%

(%)

AIC

BIC

FI

AAE

3

0.6823

6.0586

−653.5176

1230.0955

0.9153

0.5424

4

0.6606

5.8658

−708.9420

1174.6712

0.9207

0.5134

5

0.6546

5.8124

− 726.2995

1157.3136

0.9219

0.5109

6

0.6326

5.6170

− 784.7394

1098.8737

0.9271

0.4858

7

0.6394

5.6778

− 765.4140

1118.1991

0.9256

0.4888

8

0.6225

5.5271

−812.0883

1071.5248

0.9295

0.4762

9

0.6286

5.5814

− 795.3290

1088.2841

0.9281

0.4803

10

0.6401

5.6840

− 763.9056

1119.7075

0.9254

0.4866