Research Article
Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme
Table 3
Comparison results for the real system.
| Model | Super-parameters | RMSE | | (Gaussian) |
| ARX model | - | 1.016 | NN [1] | - | 0.467 | WN [6] | - | 0.529 |
| SVR + quasi-linear kernel | 1 | - | 0.462 | 5 | - | 0.487 | 10 | - | 0.491 |
| SVR + Gaussian kernel | 1
| 0.05 | 1.060 | 0.1 | 0.828 | 0.2 | 0.643 | 0.5 | 1.122 | | 0.05 | 0.850 | 0.1 | 0.740 | 0.2 | 0.562 | 0.5 | 0.633 | 10
| 0.05 | 0.775 | 0.1 | 0.665 | 0.2 | 0.608 | 0.5 | 1.024 |
| Q-ARX SVR [13] | 1
| 0.05 | 0.737 | 0.1 | 0.592 | 0.2 | 0.801 | 0.5 | 0.711 | 5
| 0.05 | 0.609 | 0.1 | 0.600 | 0.2 | 0.715 | 0.5 | 0.890 | 10
| 0.05 | 0.593 | 0.1 | 0.632 | 0.2 | 1.231 | 0.5 | 1.285 |
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