A new kernel-based approach to system identification with quantized output data

Bottegal, Giulio (author): Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

Pillonetto, Gianluigi (author): Department of Information Engineering, University of Padova, Padova, Italy

(creator_code:org_t)

Elsevier BV, 2017
2017
English.
In: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 85, s. 145-152

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Abstract Subject headings

In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. This serves as a starting point to cast our system identification problem into a Bayesian framework. We employ Markov Chain Monte Carlo methods to provide an estimate of the system. In particular, we design two methods based on the so-called Gibbs sampler that allow also to estimate the kernel hyperparameters by marginal likelihood maximization via the expectation-maximization method. Numerical simulations show the effectiveness of the proposed scheme, as compared to the state-of-the-art kernel-based methods when these are employed in system identification with quantized data. (C) 2017 Elsevier Ltd. All rights reserved.

By the author/editor: Bottegal, Giulio; Hjalmarsson, Håk ...; Pillonetto, Gian ...

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Control Engineer ...

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