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Sökning: WFRF:(Hjalmarsson Håkan) > (2015-2018) > Pillonetto G.

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1.
  • Bottegal, Giulio, et al. (författare)
  • Bayesian kernel-based system identification with quantized output data
  • 2015
  • Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 48:28, s. 455-460
  • Tidskriftsartikel (refereegranskat)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 (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.
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2.
  • Bottegal, Giulio, et al. (författare)
  • Outlier robust kernel-based system identification using l1-Laplace techniques
  • 2015
  • Ingår i: 2015 54th IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2109-2114
  • Konferensbidrag (refereegranskat)abstract
    • Regularized kernel-based methods for system identification have gained popularity in recent years. However, current formulations are not robust with respect to outliers. In this paper, we study possible solutions to robustify kernel-based methods that rely on modeling noise using the Laplacian probability density function (pdf). The contribution of this paper is two-fold. First, we introduce a new outlier robust kernel-based system identification method. It exploits the representation of Laplacian pdfs as scale mixture of Gaussians. The hyperparameters characterizing the problem are chosen using a new maximum a posteriori estimator whose solution is computed using a novel iterative scheme based on the expectation-maximization method. The second contribution of the paper is the review of two other robust kernel-based methods. The three methods are compared by means of numerical experiments, which show that all of them give substantial performance improvements compared to standard kernel-based methods for linear system identification.
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  • Resultat 1-2 av 2
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tidskriftsartikel (1)
konferensbidrag (1)
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refereegranskat (2)
Författare/redaktör
Bottegal, Giulio (2)
Pillonetto, G. (2)Ta bort avgränsningen
Hjalmarsson, Håkan (1)
Hjalmarsson, Håkan, ... (1)
Aravkin, A.Y. (1)
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Kungliga Tekniska Högskolan (2)
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Engelska (2)
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2015 (2)

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