Abstract Models of inverse systems are commonly encountered in control, eg, feedforward.
The aim of this paper is to address several aspects in identification of inverse models …

This paper describes a new kernel-based approach for linear system identification of stable
systems. We model the impulse response as the realization of a Gaussian process whose …

Inversion plays an important role in both control and parameter estimation. Control can be
viewed as system inversion since the objective is to determine the input to a system, given a …

In some recent works, an alternative nonparametric paradigm to linear model identification
has been proposed, where the unknown system impulse response is interpreted as a …

This article introduces a computational framework to identify nonlinear input–output
operators that fit a set of system trajectories while satisfying incremental integral quadratic …

All the approaches for hybrid system identification appeared in the literature assume that
model complexity is known. Popular models are eg piecewise ARX with a priori fixed orders …

Learning from examples is one of the key problems in science and engineering. It deals with
function reconstruction from a finite set of direct and noisy samples. Regularization in …

In this paper we propose an approximated method for the design of feedforward actions
based on model stable inversion. In the proposed framework the solution of the model stable …

This paper considers the problem of how to estimate a model of the inverse of a system. The
use of inverse systems can be found in many applications, such as feedforward control and …

Nonstationary inverse problems are usually cast in the state-space formalism. The complete
statistics of linear Gaussian problems can be computed with the Kalman filters and …