Abstract
In recent years, support vector regression (SVR) has attracted much attention for nonlinear system identification. It can solve nonlinear problems in the form of linear expressions within the linearly transformed space. Commonly, the convenient kernel trick is applied, which leads to implicit nonlinear mapping by replacing the inner product with a positive definite kernel function. However, only a limited number of kernel functions have been found to work well for the real applications. Moreover, it has been pointed that the implicit nonlinear kernel mapping is not always good, since it may faces the potential over-fitting for some complex and noised learning task. In this paper, explicit nonlinear mapping is learnt by means of the quasi-ARX modeling, and the associated inner product kernel, which is named quasi-linear kernel, is formulated with nonlinearity tunable between the linear and nonlinear kernel functions. Numerical and real systems are simulated to show effectiveness of the quasi-linear kernel, and the proposed identification method is also applied to microarray missing value imputation problem.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
| DOIs | |
| Publication status | Published - 2012 |
| Event | 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD Duration: 2012 Jun 10 → 2012 Jun 15 |
Other
| Other | 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 |
|---|---|
| City | Brisbane, QLD |
| Period | 12/6/10 → 12/6/15 |
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ASJC Scopus subject areas
- Software
- Artificial Intelligence
Cite this
Nonlinear system identification based on SVR with quasi-linear kernel. / Cheng, Yu; Furuzuki, Takayuki.
Proceedings of the International Joint Conference on Neural Networks. 2012. 6252694.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Nonlinear system identification based on SVR with quasi-linear kernel
AU - Cheng, Yu
AU - Furuzuki, Takayuki
PY - 2012
Y1 - 2012
N2 - In recent years, support vector regression (SVR) has attracted much attention for nonlinear system identification. It can solve nonlinear problems in the form of linear expressions within the linearly transformed space. Commonly, the convenient kernel trick is applied, which leads to implicit nonlinear mapping by replacing the inner product with a positive definite kernel function. However, only a limited number of kernel functions have been found to work well for the real applications. Moreover, it has been pointed that the implicit nonlinear kernel mapping is not always good, since it may faces the potential over-fitting for some complex and noised learning task. In this paper, explicit nonlinear mapping is learnt by means of the quasi-ARX modeling, and the associated inner product kernel, which is named quasi-linear kernel, is formulated with nonlinearity tunable between the linear and nonlinear kernel functions. Numerical and real systems are simulated to show effectiveness of the quasi-linear kernel, and the proposed identification method is also applied to microarray missing value imputation problem.
AB - In recent years, support vector regression (SVR) has attracted much attention for nonlinear system identification. It can solve nonlinear problems in the form of linear expressions within the linearly transformed space. Commonly, the convenient kernel trick is applied, which leads to implicit nonlinear mapping by replacing the inner product with a positive definite kernel function. However, only a limited number of kernel functions have been found to work well for the real applications. Moreover, it has been pointed that the implicit nonlinear kernel mapping is not always good, since it may faces the potential over-fitting for some complex and noised learning task. In this paper, explicit nonlinear mapping is learnt by means of the quasi-ARX modeling, and the associated inner product kernel, which is named quasi-linear kernel, is formulated with nonlinearity tunable between the linear and nonlinear kernel functions. Numerical and real systems are simulated to show effectiveness of the quasi-linear kernel, and the proposed identification method is also applied to microarray missing value imputation problem.
UR - http://www.scopus.com/inward/record.url?scp=84865071893&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84865071893&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2012.6252694
DO - 10.1109/IJCNN.2012.6252694
M3 - Conference contribution
AN - SCOPUS:84865071893
SN - 9781467314909
BT - Proceedings of the International Joint Conference on Neural Networks
ER -