Table 9.
Training of data using Neural Network for direct axis component.
| Sl. No. | Parameter | Remarks |
| Sample-1 | ||
| 01 | Epoch-1 | 215 Iteration |
| 02 | Time in sec | 0.02 Sec |
| 03 | Performance validation | 3.92 |
| 04 | Gradient(slope) | 1.04 |
| 05 | MU level | 1.11 |
| 06 | Validation Checker | 11 |
| 07 | Plotting Interval | 93 epoch |
| 08 | Data | Indexing |
| 09 | Training time | Levenberg-Marquardt |
| 10 | Performance check-1 | Mean square error |
| Sample-2 | ||
| 01 | Epoch-1 | 213 Iteration |
| 02 | Time in sec | 0.025 Sec |
| 03 | Performance validation | 3.97 |
| 04 | Gradient(slope) | 1.03 |
| 05 | MU level | 1.01 |
| 06 | Validation Checker | 17 |
| 07 | Plotting Interval | 97 epoch |
| 08 | Data | Indexing |
| 09 | Training time | Levenberg-Marquardt |
| 10 | Performance check-2 | Mean square error |
| Sample-3 | ||
| 01 | Epoch-2 | 198 Iteration |
| 02 | Time in sec | 0.01 Sec |
| 03 | Performance validation | 3.86 |
| 04 | Gradient(slope) | 1.03 |
| 05 | MU level | 1.01 |
| 06 | Validation Checker | 17 |
| 07 | Plotting Interval | 97 epoch |
| 08 | Data | Indexing |
| 09 | Training time | Levenberg-Marquardt |
| 10 | Performance check-2 | Mean square error |