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Träfflista för sökning "WFRF:(Hjalmarsson Håkan) srt2:(2015-2018)"

Sökning: WFRF:(Hjalmarsson Håkan) > (2015-2018)

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  • Abdalmoaty, Mohamed, et al. (författare)
  • On Re-Weighting, Regularization Selection, and Transient in Nuclear Norm Based Identification
  • 2015
  • Konferensbidrag (refereegranskat)abstract
    • In this contribution, we consider the classical problem of estimating an Output Error model given a set of input-output measurements. First, we develop a regularization method based on the re-weighted nuclear norm heuristic. We show that the re-weighting improves the estimate in terms of better fit. Second, we suggest an implementation method that helps in eliminating the regularization parameters from the problem by introducing a constant based on a validation criterion. Finally, we develop a method for considering the effect of the transient when the initial conditions are unknown. A simple numerical example is used to demonstrate the proposed method in comparison to classical and another recent method based on the nuclear norm heuristic.
  • Abdalmoaty, Mohamed, 1986-, et al. (författare)
  • Simulated Pseudo Maximum Likelihood Identification of Nonlinear Models
  • 2017
  • Ingår i: The 20th IFAC World Congress. - Elsevier. ; s. 14058-14063
  • Konferensbidrag (refereegranskat)abstract
    • Nonlinear stochastic parametric models are widely used in various fields. However, for these models, the problem of maximum likelihood identification is very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the analytically intractable likelihood function and compute either the maximum likelihood or a Bayesian estimator. These methods, albeit asymptotically optimal, are computationally expensive. In this contribution, we present a simulation-based pseudo likelihood estimator for nonlinear stochastic models. It relies only on the first two moments of the model, which are easy to approximate using Monte-Carlo simulations on the model. The resulting estimator is consistent and asymptotically normal. We show that the pseudo maximum likelihood estimator, based on a multivariate normal family, solves a prediction error minimization problem using a parameterized norm and an implicit linear predictor. In the light of this interpretation, we compare with the predictor defined by an ensemble Kalman filter. Although not identical, simulations indicate a close relationship. The performance of the simulated pseudo maximum likelihood method is illustrated in three examples. They include a challenging state-space model of dimension 100 with one output and 2 unknown parameters, as well as an application-motivated model with 5 states, 2 outputs and 5 unknown parameters.
  • Bombois, X., et al. (författare)
  • Optimal identification experiment design for the interconnection of locally controlled systems
  • 2018
  • Ingår i: Automatica. - Elsevier. - 0005-1098. ; 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. 
  • Bottegal, Giulio, et al. (författare)
  • A new kernel-based approach to system identification with quantized output data
  • 2017
  • Ingår i: Automatica. - 0005-1098. ; 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.
  • 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.
  • Bottegal, Giulio, et al. (författare)
  • Blind system identification using kernel-based methods
  • 2015
  • 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.
  • 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.
  • 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.
  • Briat, C., et al. (författare)
  • The Conservation of Information, Towards an Axiomatized Modular Modeling Approach to Congestion Control
  • 2015
  • Ingår i: IEEE/ACM Transactions on Networking. - 1063-6692. ; 23:3, s. 851-865
  • Tidskriftsartikel (refereegranskat)abstract
    • We derive a modular fluid-flow network congestion control model based on a law of fundamental nature in networks: the conservation of information. Network elements such as queues, users, and transmission channels and network performance indicators like sending/acknowledgment rates and delays are mathematically modeled by applying this law locally. Our contributions are twofold. First, we introduce a modular metamodel that is sufficiently generic to represent any network topology. The proposed model is composed of building blocks that implement mechanisms ignored by the existing ones, which can be recovered from exact reduction or approximation of this new model. Second, we provide a novel classification of previously proposed models in the literature and show that they are often not capable of capturing the transient behavior of the network precisely. Numerical results obtained from packet-level simulations demonstrate the accuracy of the proposed model.
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