Research Article
Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme
Table 1
Results of different models for Mackey-Glass time series prediction.
| | Model | Method | Number of neurons | RMSE |
| | Autoregressive model | Least square | 5 | 0.19 | | FNT [8] | PIPE | Not provided | 7.1 × 10−3 | | RBF-AR [9] | SNPOM | 25 | 5.8 × 10−4 | | LLWNN [10] | PSO + gradient decent algorithm | 10 | 3.6 × 10−3 | | RBF [11] | -means clustering | 238 | 1.3 × 10−3 | | RBF [12] | GA | 98 | 1.5 × 10−3 |
| | Quasi-ARX RBFN model | Proposed | 20 | 9.1 × 10−3 | | Quasi-ARX RBFN model | Proposed + SNPOM | 20 | 2.1 × 10−3 |
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