Name
Abstract | ||
|---|---|---|
Aiming at nonlinear decontrolled plants at large exist in industrial processes, this paper firstly introduces the support vector machine and least squares support vector machine briefly. On this basis, we propose a nonlinear generalized predictive control model based on least squares support vector machines. This method can overcome the classic quadratic programming method for solving support vector machines curse of dimensionality problem, and has a good robustness, suitable for large-scale computing. So use least squares support vector machines as nonlinear predictive model have more advantages. © 2012 IEEE. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICNC.2012.6234606 | ICNC |
Keywords | Field | DocType |
generalized predictive control,nonlinear system,support vector machines,predictive control,prediction algorithms,quadratic program,least squares support vector machine,predictive models,support vector machine,quadratic programming,prediction model,curse of dimensionality,mathematical model,computational modeling | Structured support vector machine,Least squares,Mathematical optimization,Least squares support vector machine,Computer science,Support vector machine,Curse of dimensionality,Robustness (computer science),Artificial intelligence,Quadratic programming,Relevance vector machine,Machine learning | Conference |
Volume | Issue | Citations |
null | null | 0 |
PageRank | References | Authors |
0.34 | 0 | 1 |