Review Article
On Training Efficiency and Computational Costs of a Feed Forward Neural Network: A Review
Table 6
Summary of adaptive strategies.
| Ref. | Method | Convergence | Precision | Computational costs | Notes |
| [34] | Scalable sigmoid (NNAAF-1) | 6960 Epochs | % error = 0.033 | Training time: 2739 s | Learning rate 0.9 |
| [34] | Sin-sigmoid (NNAAF-2) | 8232 Epochs | % error = 0.045 | Training time: 2080 s | Learning rate 0.9 |
| [34] | Morlet wavelet (NNAAF-3) | 10000 Epochs | % error = 0.097 | Training time: 3046 s | Learning rate 0.2 |
| [35] | Sigmoid-radial-sin | 5250 Epochs | N-RMSE = 0.09301 | N/A | Trained with Levenberg Marquardt Algorithm |
| [36] | Sin-sigmoid | 5000–9000 Epochs | 89.60–94.3% accuracy (classification problem) | N/A | Implemented on higher order NN (HONN) |
| [37] | Trainable AF | 20000 Epochs | RMSE = −35 dB | N/A | |
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