Table 4.
Number of nodes | Training data set | |||
---|---|---|---|---|
Hyperbolic tangent function | Radial basis function | |||
R2 | RMSE | R2 | RMSE | |
1 | 0.921 | 75 | 0.918 | 77 |
2 | 0.932 | 70 | 0.936 | 68 |
3 | 0.942 | 64 | 0.941 | 65 |
4 | 0.941 | 65 | 0.964* | 51* |
5 | 0.952 | 59 | 0.958 | 55 |
6 | 0.948 | 61 | 0.955 | 57 |
7 | 0.948 | 61 | 0.952 | 58 |
8 | 0.942 | 65 | 0.945 | 63 |
9 | 0.944 | 63 | 0.951 | 59 |
10 | 0.953 | 58 | 0.953 | 58 |
RMSE root mean square error.
* Means the best performance of ANN models with different numbers of nodes and activation functions to predict ADG.
1 All the ANN models were generated using the training data set (n = 287).