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Table 4
Hyperparameter details, dataset statistics, and cross-validation results.
| Hyperparameter details |
|
|---|---|
| Particulars | Description |
| Number of layers | Total layers (input, hidden, output) |
| Number of neurons per layer | Input layer: 02 neurons |
| Hidden layer: varied for optimum network (06 neurons) | |
| Output layer: 05 neurons | |
| Activation functions | Hyperbolic tangent for hidden layer |
| Linear function for output layer | |
| Training algorithm | Levenberg-Marquardt |
| Iterations | 50 |
| Learning rate | 0.05 |
| Epochs | 300 |
| Loss function | Measures stop training when mean squared error (MSE) reaches 10−5 |
| Batch size | Number of samples per update |
| Regularization | Prevents overfitting |
| Weight initialization | Random |
| Dataset statistics |
|
| Particulars | Description |
| Number of samples | Total number of data points (1000) |
| Number of features | Inputs(2) and Outputs(5) |
| Feature range | Min and Max of input/output values |
| Data extraction | Extract inputs/outputs |
| Data normalization | Zero mean and unit variance. |
| Data splitting | Random (70% Training set, 15% Validation set, 15% Test set |
| Cross-validation results |
|
| Particulars | Description |
| Training performance | Minimum training error (0.1899) |
| Validation performance | Minimum validation error (0.1919) |
| Test performance | Minimum test error (0.126) |
| Goodness of fit | Correlation coefficient (R) |
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