Name
Abstract | ||
|---|---|---|
An adaptive inverse control algorithm is proposed by combining fast online support vector machine regression (SVR) algorithm with straight inverse control algorithm. Because training speed of standard online SVR algorithm is very slow, a kernel cache-based method is developed to accelerate the standard algorithm and a new fast online SVR algorithm is obtained. Then the new algorithm is applied in straight inverse control for constructing the inverse model of controlled system online, and output errors of the system are used to control online SVR algorithm, which made the whole control system a closed-loop one. Simulation results show that the new algorithm has good control performance. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.neucom.2006.10.021 | Neurocomputing |
Keywords | Field | DocType |
adaptive inverse control algorithm,controlled system online,new algorithm,good control performance,kernel cache,straight inverse control algorithm,standard online svr algorithm,closed-loop system,standard algorithm,straight inverse control,online svr algorithm,online svm regression,whole control system,support vector machine,inverse modeling,control system | Standard algorithms,Linde–Buzo–Gray algorithm,Computer science,FSA-Red Algorithm,Artificial intelligence,Control system,Population-based incremental learning,Kernel (linear algebra),Inverse,Ramer–Douglas–Peucker algorithm,Pattern recognition,Algorithm,Machine learning | Journal |
Volume | Issue | ISSN |
70 | 4-6 | Neurocomputing |
Citations | PageRank | References |
17 | 0.81 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Wang | 1 | 177 | 43.68 |
| Daoying Pi | 2 | 50 | 9.21 |
| Youxian Sun | 3 | 2707 | 196.15 |