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arXiv · 1610.00470

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

Giulio Bottegal·Håkan Hjalmarsson·Gianluigi Pillonetto
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Abstract

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.

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Giulio Bottegal, Håkan Hjalmarsson, Gianluigi Pillonetto. 2016-10-03. A new kernel-based approach to system identification with quantized output data. https://arxiv.org/abs/1610.00470

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