Name
Abstract | ||
|---|---|---|
Nonlinear system identification is considered using a generalized kernel regression model. Unlike the standard kernel model, which employs a fixed common variance for all the kernel regressors, each kernel regressor in the generalized kernel model has an individually tuned diagonal covariance matrix that is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. An efficient construction algorithm based on orthogonal forward regression with leave-one-out (LOO) test statistic and local regularization (LR) is then used to select a parsimonious generalized kernel regression model from the resulting full regression matrix. The proposed modeling algorithm is fully automatic and the user is not required to specify any criterion to terminate the construction procedure. Experimental results involving two real data sets demonstrate the effectiveness of the proposed nonlinear system identification approach. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TCST.2004.841652 | IEEE Trans. Contr. Sys. Techn. |
Keywords | Field | DocType |
Nonlinear systems,Kernel,Covariance matrix,Optimization methods,Training data,Boosting,Least squares methods,System identification,Testing,Statistical analysis | Principal component regression,Radial basis function kernel,Control theory,Kernel principal component analysis,Polynomial kernel,Artificial intelligence,Kernel regression,Kernel embedding of distributions,Algorithm,Variable kernel density estimation,Mathematics,Machine learning,Kernel (statistics) | Journal |
Volume | Issue | ISSN |
13 | 3 | 1063-6536 |
Citations | PageRank | References |
12 | 1.05 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sheng Chen | 1 | 1813 | 230.12 |
| X. Hong | 2 | 797 | 58.71 |
| C. J. Harris | 3 | 132 | 7.59 |
| Xunxian Wang | 4 | 50 | 3.78 |