Parameter setting for TA-NLARNN model and GRNN model
| Parameter | NLARNN model | GRNN model |
|---|---|---|
| Network Type | Recurrent Neural Network (RNN) | Recurrent Neural Network (RNN) |
| Activation Function | Hidden layer activation function:Hyperbolic Tangent (tanh) Output layer activation function: Linear | Hidden layer activation function:Gaussian Output layer activation function: Linear |
| Hidden Units | 5–25 | 50 |
| Learning Rate | 0.001–0.01 | 0.001–0.1 |
| Training Algorithm | LM. BR, CGF, SCG | BR |
| Input Scaling | Standardize or Normalize | Normalize |
| Loss Function | Root Mean Squared Error (RMSE) | Root Mean Squared Error (RMSE) |
| Epochs | Typically 100–1,000 | Typically 50–500 |
| Early Stopping | Enabled | Enabled |
| Spread | N/A | 0–10 |
| Feedback delay | 1–4 | N/A |
| Parameter | NLARNN model | GRNN model |
|---|---|---|
| Network Type | Recurrent Neural Network (RNN) | Recurrent Neural Network (RNN) |
| Activation Function | Hidden layer activation function:Hyperbolic Tangent (tanh) | Hidden layer activation function:Gaussian |
| Hidden Units | 5–25 | 50 |
| Learning Rate | 0.001–0.01 | 0.001–0.1 |
| Training Algorithm | LM. BR, CGF, SCG | BR |
| Input Scaling | Standardize or Normalize | Normalize |
| Loss Function | Root Mean Squared Error (RMSE) | Root Mean Squared Error (RMSE) |
| Epochs | Typically 100–1,000 | Typically 50–500 |
| Early Stopping | Enabled | Enabled |
| Spread | N/A | 0–10 |
| Feedback delay | 1–4 | N/A |
Sharing content requires targeting cookies to be enabled. Please update your cookie preferences to use this feature.