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Sökning: WFRF:(Hjalmarsson Håkan)

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41.
  • Bombois, Xavier, et al. (författare)
  • Optimal identification experiment design for the interconnection of locally controlled systems
  • 2018
  • Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 89, s. 169-179
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper considers the identification of the modules of a network of locally controlled systems (multi-agent systems). Its main contribution is to determine the least perturbing identification experiment that will nevertheless lead to sufficiently accurate models of each module for the global performance of the network to be improved by a redesign of the decentralized controllers. Another contribution is to determine the experimental conditions under which sufficiently informative data (i.e. data leading to a consistent estimate) can be collected for the identification of any module in such a network. 
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42.
  • Bombois, Xavier, et al. (författare)
  • Optimal input design for robust H2 deconvolution filtering
  • 2009
  • Ingår i: 15th IFAC Symposium on System Identification, SYSID 2009. - : Elsevier BV. ; , s. 934-939
  • Konferensbidrag (refereegranskat)abstract
    • Deconvolution filtering where the system and noise dynamics are obtained by parametric system identification is considered. Consistent with standard identification methods, ellipsoidal uncertainty in the estimated parameters is considered. Three problems are considered: 1) Computation of the worst case H2 performance of a given deconvolution filter in this uncertainty set. 2) Design of a filter which minimizes the worst case H2 performance in this uncertainty set. 3) Input design for the identification experiment, subject to a limited input power budget, such that the filter in 2) gives the smallest possible worst-case H2 performance. It is shown that there are convex relaxations of the optimization problems corresponding to 1) and 2) while the third problem can be treated via iterating between two convex optimization problems.
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43.
  • Bombois, Xavier, et al. (författare)
  • Robust optimal identification experiment design for multisine excitation
  • 2021
  • Ingår i: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 125
  • Tidskriftsartikel (refereegranskat)abstract
    • In least costly experiment design, the optimal spectrum of an identification experiment is determined in such a way that the cost of the experiment is minimized under some accuracy constraint on the identified parameter vector. Like all optimal experiment design problems, this optimization problem depends on the unknown true system, which is generally replaced by an initial estimate. One important consequence of this is that we can underestimate the actual cost of the experiment and that the accuracy of the identified model can be lower than desired. Here, based on an a-priori uncertainty set for the true system, we propose a convex optimization approach that allows to prevent these issues from happening. We do this when the to-be-determined spectrum is the one of a multisine signal.
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44.
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45.
  • Bottegal, Giulio, et al. (författare)
  • A new kernel-based approach to system identification with quantized output data
  • 2017
  • Ingår i: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 85, s. 145-152
  • 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 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.
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46.
  • 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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47.
  • Bottegal, Giulio, et al. (författare)
  • Blind system identification using kernel-based methods
  • 2015
  • Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963.
  • Konferensbidrag (refereegranskat)abstract
    • We propose a new method for blind system identification (BSI). Resorting to a Gaussian regression framework, we model the impulse response of the unknown linear system as a realization of a Gaussian process. The structure of the covariance matrix (or kernel) of such a process is given by the stable spline kernel, which has been recently introduced for system identification purposes and depends on an unknown hyperparameter. We assume that the input can be linearly described by few parameters. We estimate these parameters, together with the kernel hyperparameter and the noise variance, using an empirical Bayes approach. The related optimization problem is efficiently solved with a novel iterative scheme based on the Expectation-Maximization (EM) method. In particular, we show that each iteration consists of a set of simple update rules. Through some numerical experiments we show that the proposed method give very promising performance.
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48.
  • Bottegal, G., et al. (författare)
  • On maximum likelihood identification of errors-in-variables models
  • 2017
  • Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 50:1, s. 2824-2829
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper, we revisit maximum likelihood methods for identification of errors-in-variables systems. We assume that the system admits a parametric description, and that the input is a stochastic ARMA process. The cost function associated with the maximum likelihood criterion is minimized by introducing a new iterative solution scheme based on the expectation-maximization method, which proves fast and easily implementable. Numerical simulations show the effectiveness of the proposed method.
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49.
  • Bottegal, Giulio, 1984-, et al. (författare)
  • On the variance of identified SIMO systems with spatially correlated output noise
  • 2014
  • Ingår i: Proceedings of the IEEE Conference on Decision and Control. - : IEEE conference proceedings. ; , s. 2636-2641
  • Konferensbidrag (refereegranskat)abstract
    • In this paper, we study the problem of evaluating the accuracy of identified linear single-input multi-output (SIMO) dynamical models, where the disturbances affecting the output measurements are spatially correlated. Assuming that the input is an observed white noise sequence, we provide an expression for the covariance matrix of the parameter estimates when weighted least-squares (WLS) are adopted to identify the parameters. Then, we show that, by describing one of the subsystems composing the SIMO structure using less parameters than the other subsystems, substantial improvement on the accuracy of the estimates of some parameters can be obtained. The amount of such an improvement depends critically on the covariance matrix of the output noise and we provide a condition on the noise correlation structure under which the mentioned model parametrization gives the lowest variance in the identified model. We illustrate the derived results through some numerical experiments.
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50.
  • 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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