Name
Abstract | ||
|---|---|---|
In this paper, a support vector machine (SVM) with polynomial kernel function enhanced nonlinear self-tuning controller is developed, which combines the SVM identifier and parameters’ modifier together. The inverse model of a nonlinear system is achieved by off-line black-box identification according to input and output data. Then parameters of the model are modified online using gradient descent algorithm. Simulation results show that SVM based self-tuning control can be well applied to nonlinear uncertain system. And the SVM based self-tuning control of nonlinear system has good robustness performance in tracking reference input with good generalization ability. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11760023_134 | ISNN (2) |
Keywords | Field | DocType |
svm identifier,uncertain system,gradient descent algorithm,inverse model,reference input,good robustness performance,self-tuning control,nonlinear system,nonlinear self-tuning controller,good generalization ability,gradient descent,inverse modeling,kernel function,support vector machine | Control theory,Gradient descent,Nonlinear system,Ranking SVM,Control theory,Nonlinear control,Computer science,Support vector machine,Algorithm,Polynomial kernel,Kernel (statistics) | Conference |
Volume | ISSN | ISBN |
3972 | 0302-9743 | 3-540-34437-3 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weimin Zhong | 1 | 79 | 14.18 |
| Daoying Pi | 2 | 50 | 9.21 |
| Chi Xu | 3 | 3 | 1.48 |
| Sizhen Chu | 4 | 3 | 1.48 |