Sparse kernel regression modelling using combined locally regularized orthogonal least squares and D-optimality experimental design
The paper proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complem...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
2003-06.
|
| Subjects: | |
| Online Access: | Get fulltext Get fulltext |