Anna Marconato

TL;DR: The regularization approach introduced recently for nonparametric estimation of linear systems is extended to the estimation of nonlinear systems modeled as Volterra series, where the kernels of order higher than one, representing higher dimensional impulse responses in the series, are considered to be realizations of multidimensional Gaussian processes.

TL;DR: This paper presents a method to identify parallel Wiener-Hammerstein systems starting from input-output data only, and the consistency of the proposed initialization procedure is proven.

TL;DR: In this article, an alternative perspective on the same problem is introduced, instead of relying on a Bayesian framework to include assumptions about the system in the definition of the covariance matrix of the parameters, here the prior knowledge is injected at the cost function level.

TL;DR: This work defines the regularisation matrix as a filtering operation on the parameters, which allows for a more intuitive formulation of the problem from an engineering point of view and results in a unified framework to model low-pass, band-pass and high-pass systems, and systems with one or more resonances.

TL;DR: This document proposes the use of a widely known learning-from-examples paradigm, namely the Support Vector Machines for Regression (SVRs), for system identification problems, and starts off with the identification of a simple linear system.