Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Artificial Intelligence Review - Special issue on lazy learning
Evaluation of gaussian processes and other methods for non-linear regression
Evaluation of gaussian processes and other methods for non-linear regression
Brief paper: Identification of dynamic systems using Piecewise-Affine basis function models
Automatica (Journal of IFAC)
Identification of Wiener systems with binary-valued output observations
Automatica (Journal of IFAC)
Piecewise linear solution paths with application to direct weight optimization
Automatica (Journal of IFAC)
Brief paper: Adaptive hinging hyperplanes and its applications in dynamic system identification
Automatica (Journal of IFAC)
Differential constraints for bounded recursive identification with multivariate splines
Automatica (Journal of IFAC)
Kernel based approaches to local nonlinear non-parametric variable selection
Automatica (Journal of IFAC)
Hi-index | 22.16 |
A general framework for estimating nonlinear functions and systems is described and analyzed in this paper. Identification of a system is seen as estimation of a predictor function. The considered predictor function estimate at a particular point is defined to be affine in the observed outputs and the estimate is defined by the weights in this expression. For each given point, the maximal mean-square error (or an upper bound) of the function estimate over a class of possible true functions is minimized with respect to the weights, which is a convex optimization problem. This gives different types of algorithms depending on the chosen function class. It is shown how the classical linear least squares is obtained as a special case and how unknown-but-bounded disturbances can be handled. Most of the paper deals with the method applied to locally smooth predictor functions. It is shown how this leads to local estimators with a finite bandwidth, meaning that only observations in a neighborhood of the target point will be used in the estimate. The size of this neighborhood (the bandwidth) is automatically computed and reflects the noise level in the data and the smoothness priors. The approach is applied to a number of dynamical systems to illustrate its potential.