Artificial Intelligence Review - Special issue on lazy learning
Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
The explicit linear quadratic regulator for constrained systems
Automatica (Journal of IFAC)
Brief An algorithm for multi-parametric quadratic programming and explicit MPC solutions
Automatica (Journal of IFAC)
Nonlinear system identification via direct weight optimization
Automatica (Journal of IFAC)
Brief paper: Segmentation of ARX-models using sum-of-norms regularization
Automatica (Journal of IFAC)
Smoothed state estimates under abrupt changes using sum-of-norms regularization
Automatica (Journal of IFAC)
Identification of switched linear regression models using sum-of-norms regularization
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
Recently, pathfollowing algorithms for parametric optimization problems with piecewise linear solution paths have been developed within the field of regularized regression. This paper presents a generalization of these algorithms to a wider class of problems. It is shown that the approach can be applied to the nonparametric system identification method, Direct Weight Optimization (DWO), and be used to enhance the computational efficiency of this method. The most important design parameter in the DWO method is a parameter (@l) controlling the bias-variance trade-off, and the use of parametric optimization with piecewise linear solution paths means that the DWO estimates can be efficiently computed for all values of @l simultaneously. This allows for designing computationally attractive adaptive bandwidth selection algorithms. One such algorithm for DWO is proposed and demonstrated in two examples.