Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Regressor and structure selection in NARX models using a structured ANOVA approach
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Model selection approaches for non-linear system identification: a review
International Journal of Systems Science
Overview and recent advances in partial least squares
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
A two-stage algorithm for identification of nonlinear dynamic systems
Automatica (Journal of IFAC)
Nonlinear system identification via direct weight optimization
Automatica (Journal of IFAC)
On the dynamical modeling with neural fuzzy networks
IEEE Transactions on Neural Networks
| Hi-index | 22.14 |
In this paper, we consider the variable selection problem for a nonlinear non-parametric system. Two approaches are proposed, one top-down approach and one bottom-up approach. The top-down algorithm selects a variable by detecting if the corresponding partial derivative is zero or not at the point of interest. The algorithm is shown to have not only the parameter but also the set convergence. This is critical because the variable selection problem is binary, a variable is either selected or not selected. The bottom-up approach is based on the forward/backward stepwise selection which is designed to work if the data length is limited. Both approaches determine the most important variables locally and allow the unknown non-parametric nonlinear system to have different local dimensions at different points of interest. Further, two potential applications along with numerical simulations are provided to illustrate the usefulness of the proposed algorithms.