TY - JOUR
T1 - A new kernel-based approach to system identification with quantized output data
AU - Bottegal, G.
AU - Hjalmarsson, H.
AU - Pillonetto, G.
PY - 2017/11/1
Y1 - 2017/11/1
N2 - 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.
AB - 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.
KW - Expectation–maximization
KW - Gibbs sampler
KW - Kernel-based methods
KW - Quantized data
KW - System identification
UR - http://www.scopus.com/inward/record.url?scp=85027880897&partnerID=8YFLogxK
U2 - 10.1016/j.automatica.2017.07.053
DO - 10.1016/j.automatica.2017.07.053
M3 - Article
AN - SCOPUS:85027880897
SN - 0005-1098
VL - 85
SP - 145
EP - 152
JO - Automatica
JF - Automatica
ER -