Bayesian kernel-based system identification with quantized output data

• 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 (MCMC) methods to provide an estimate of the system. In particular, we show how to design a Gibbs sampler which quickly converges to the target distribution. Numerical simulations show a substantial improvement in the accuracy of the estimates over state-of-the-art kernel-based methods when employed in identification of systems with quantized data.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Nyckelord

Impulse response

Linear systems

Markov processes

Numerical methods

Religious buildings

Bayesian frameworks

Gibbs samplers

Identification of systems

Kernel based methods

Markov chain Monte Carlo method

State of the art

System identification problems

Zero mean Gaussian process

Monte Carlo methods

Publikations- och innehållstyp

ref (ämneskategori)

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