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