Name
Title | ||
|---|---|---|
On-Line Identification of Continuous-Time Nonlinear Systems Using Gaussian Process Models. |
Abstract | ||
|---|---|---|
This paper deals with an on-line identification of continuous-time nonlinear systems using a moving-window type Gaussian process (GP) model. The GP is a Gaussian random function and is completely described by its mean function and covariance function. In order to track the time-varying system parameters and nonlinear function, the linear recursive least-squares (RLS) method is combined with firefly algorithm (FA) in a bootstrap manner. The hyperparameters of the covariance function are searched for by FA, while the system parameters of the linear terms and the weighting parameters of the mean function arc updated by the RLS method. Numerical experiments are carried out to demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SCIS-ISIS.2018.00191 | Joint International Conference on Soft Computing and Intelligent Systems SCIS and International Symposium on Advanced Intelligent Systems ISIS |
Keywords | Field | DocType |
identification,on-line,nonlinear system,Gaussian process model,firefly algorithm | Mathematical optimization,Nonlinear system,Computer science,Algorithm,Gaussian process | Conference |
ISSN | Citations | PageRank |
2377-6870 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Koichi Sugiyama | 1 | 0 | 0.34 |
| Tomohiro Hachino | 2 | 8 | 2.37 |