Title
A new kernel-based approach for linear system identification
Abstract
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 statistics, differently from previously adopted priors, include information not only on smoothness but also on BIBO-stability. The associated autocovariance defines what we call a stable spline kernel. The corresponding minimum variance estimate belongs to a reproducing kernel Hilbert space which is spectrally characterized. Compared to parametric identification techniques, the impulse response of the system is searched for within an infinite-dimensional space, dense in the space of continuous functions. Overparametrization is avoided by tuning few hyperparameters via marginal likelihood maximization. The proposed approach may prove particularly useful in the context of robust identification in order to obtain reduced order models by exploiting a two-step procedure that projects the nonparametric estimate onto the space of nominal models. The continuous-time derivation immediately extends to the discrete-time case. On several continuous- and discrete-time benchmarks taken from the literature the proposed approach compares very favorably with the existing parametric and nonparametric techniques.
Year
DOI
Venue
2010
10.1016/j.automatica.2009.10.031
Automatica
Keywords
Field
DocType
Linear system identification,Kernel-based methods,Bayesian estimation,Regularization,Gaussian processes,Robust identification,Stochastic embedding
Kernel (linear algebra),Mathematical optimization,Kernel embedding of distributions,Algorithm,Kernel principal component analysis,Gaussian process,Kernel method,System identification,Variable kernel density estimation,Reproducing kernel Hilbert space,Calculus,Mathematics
Journal
Volume
Issue
ISSN
46
1
Automatica
Citations 
PageRank 
References 
109
6.23
19
Authors
2
Search Limit
100109
Name
Order
Citations
PageRank
Pillonetto Gianluigi187780.84
Giuseppe De Nicolao273876.26