Research Article
Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme
Table 2
Simulated results of the SVR-based methods for rational system.
| Method | Super-parameters | RMSE | | (Gaussian) | Short training sequence | Long training sequence |
| Proposed | 1 | - | 0.0546 | 0.0287 | 10 | - | 0.0379 | 0.0216 | 100 | - | 0.0423 | 0.0216 |
| SVR + linear kernel | 1 | - | 0.0760 | 0.0710 | 10 | - | 0.0764 | 0.0708 | 100 | - | 0.0763 | 0.0708 |
| SVR + Gaussian kernel | 1
| 0.01 | 0.1465 | 0.0560 | 0.05 | 0.0790 | 0.0426 | 0.1 | 0.0808 | 0.0421 | 0.5 | 0.0895 | 0.0279 | 10
| 0.01 | 0.0782 | 0.0376 | 0.05 | 0.0722 | 0.0409 | 0.1 | 0.0866 | 0.0365 | 0.5 | 0.0699 | 0.0138 | 100
| 0.01 | 0.0722 | 0.0352 | 0.05 | 0.0859 | 0.0376 | 0.1 | 0.0931 | 0.0313 | 0.5 | 0.1229 | 0.0340 |
| Q-ARX SVR [13] | 1
| 0.01 | 0.0698 | 0.0362 | 0.05 | 0.0791 | 0.0384 | 0.1 | 0.0857 | 0.0345 | 0.5 | 0.0749 | 0.0116 | 10
| 0.01 | 0.0783 | 0.0412 | 0.05 | 0.0918 | 0.0328 | 0.1 | 0.0922 | 0.0242 | 0.5 | 0.1483 | 0.0338 | 100
| 0.01 | 0.0872 | 0.0400 | 0.05 | 0.1071 | 0.0237 | 0.1 | 0.8186 | 0.0166 | 0.5 | 0.1516 | 0.0487 |
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