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Table 6 The average of fitting criteria of RMSE, RMSE%, AIC, BIC, FI and AAE according to the number of Neurons

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 neurons

RMSE

RMSE%

(%)

AIC

BIC

FI

AAE

10

0.7182

6.3773

− 564.3337

1319.2794

0.9063

0.5763

20

0.6704

5.9532

−682.1601

1201.4530

0.9184

0.5254

30

0.6581

5.8435

− 714.6754

1168.9378

0.9213

0.5164

40

0.6473

5.7474

− 744.2704

1139.3427

0.9238

0.4994

50

0.6532

5.8004

− 727.2888

1156.3244

0.9225

0.5068

60

0.6414

5.6953

−759.3048

1124.3084

0.9252

0.4942

70

0.6219

5.5218

−812.3812

1071.2320

0.9297

0.4735

80

0.6157

5.4675

− 829.2568

1054.3563

0.9311

0.4649

90

0.6195

5.5007

−819.3154

1064.2977

0.9302

0.4701

100

0.6051

5.3732

−859.8078

1023.8053

0.9334

0.4536