Complexity / 2017 / Article / Tab 2

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 -0.03790.0216
100 - 0.0423 0.0216

SVR + linear kernel 1 -0.0760 0.0710
10 - 0.07640.0708
100 - 0.0763 0.0708

SVR + Gaussian kernel 1
0.01 0.1465 0.0560
0.05 0.0790 0.0426
0.1 0.08080.0421
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
0.50.06990.0138
100
0.010.0722 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
0.010.0698 0.0362
0.05 0.0791 0.0384
0.1 0.0857 0.0345
0.50.07490.0116
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

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.