| 1. |
- Bottegal, Giulio, et al.
(författare)
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Blind system identification using kernel-based methods
- 2015
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Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963.
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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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| 2. |
- Bottegal, G., et al.
(författare)
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On maximum likelihood identification of errors-in-variables models
- 2017
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Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 50:1, s. 2824-2829
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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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| 3. |
- Della Penda, Demia, et al.
(författare)
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Optimal Power Control for D2D Communications under Rician Fading: a Risk Theoretical Approach
- 2017
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Ingår i: 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509050192
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Konferensbidrag (refereegranskat)abstract
- Device-to-device communication is a technology that allows users in close proximity to establish a direct communication link instead of passing through the base station. Because direct communications are likely to have a strong line-of-sight component in the received signal, it is reasonable to model the direct channel with Rician fading. In this paper, we propose a power-control scheme for device-to-device communications on a shared channel. Our allocation minimizes the total power consumption while limiting the link outage probability due to Rician fast fading. By leveraging the concept of conditional-value-at-risk from the field of finance, we obtain a linear programming formulation which can be efficiently solved. Through simulation results we show the benefit of the proposed power allocation compared to a deterministic power control that does not account for the random channel variations. Moreover, we provide insights into how the network topology and the parameter settings affect the performance and feasibility of the power allocation.
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| 4. |
- Risuleo, Riccardo Sven, 1986-, et al.
(författare)
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A benchmark for data-based office modeling: challenges related to CO2 dynamics
- 2015
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Ingår i: IFAC-PapersOnLine. - : IFAC Papers Online. - 2405-8963. ; , s. 1256-1261
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Konferensbidrag (refereegranskat)abstract
- This paper describes a benchmark consisting of a set of synthetic measurements relative to an office environment simulated with the software IDA-ICE. The simulated environment reproduces a laboratory at the KTH-EES Smart Building, equipped with a building management system. The data set contains measurement records collected over a period of several days. The signals correspond to CO2 concentration, mechanical ventilation airows, air infiltrations and occupancy. Information on door and window opening is also available. This benchmark is intended for testing data-based modeling techniques. The ultimate goal is the development of models to improve the forecast and control of environmental variables. Among the numerous challenges related to this framework, we focus on the problem of occupancy estimation using information on CO2 concentration, which we treat as a blind identification problem. For benchmarking purposes, we present two different identification approaches: a baseline overparameterization method and a kernel-based method.
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| 5. |
- Risuleo, Riccardo Sven, 1986-, et al.
(författare)
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A kernel-based approach to Hammerstein system identication
- 2015
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Ingår i: IFAC-PapersOnLine. - : IFAC Papers Online. - 2405-8963. ; , s. 1011-1016
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Konferensbidrag (refereegranskat)abstract
- n this paper, we propose a novel algorithm for the identification of Hammerstein systems. Adopting a Bayesian approach, we model the impulse response of the unknown linear dynamic system as a realization of a zero-mean Gaussian process. The covariance matrix (or kernel) of this process is given by the recently introduced stable-spline kernel, which encodes information on the stability and regularity of the impulse response. The static nonlinearity of the model is identified using an Empirical Bayes approach, i.e. by maximizing the output marginal likelihood, which is obtained by integrating out the unknown impulse response. The related optimization problem is solved adopting a novel iterative scheme based on the Expectation-Maximization method, where each iteration consists in a simple sequence of update rules. Numerical experiments show that the proposed method compares favorably with a standard algorithm for Hammerstein system identification.
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| 6. |
- Risuleo, Riccardo Sven, 1986-, et al.
(författare)
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A new kernel-based approach to overparameterized Hammerstein system identification
- 2015
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Ingår i: 2015 54th IEEE Conference on Decision and Control (CDC). - : IEEE conference proceedings. - 9781479978847 ; , s. 115-120
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Konferensbidrag (refereegranskat)abstract
- Download CitationEmailPrintRequest PermissionsThe object of this paper is the identification of Hammerstein systems, which are dynamic systems consisting of a static nonlinearity and a linear time-invariant dynamic system in cascade. We assume that the nonlinear function can be described as a linear combination of p basis functions. We model the system dynamics by means of an np-dimensional vector. This vector, usually referred to as overparameterized vector, contains all the combinations between the nonlinearity coefficients and the first n samples of the impulse response of the linear block. The estimation of the overparameterized vector is performed with a new regularized kernel-based approach. To this end, we introduce a novel kernel tailored for overparameterized models, which yields estimates that can be uniquely decomposed as the combination of an impulse response and p coefficients of the static nonlinearity. As part of the work, we establish a clear connection between the proposed identification scheme and our recently developed nonparametric method based on the stable spline kernel.
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| 7. |
- Risuleo, Riccardo Sven, 1986-, et al.
(författare)
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A nonparametric kernel-based approach to Hammerstein system identification
- 2017
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Ingår i: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 85, s. 234-247
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Tidskriftsartikel (refereegranskat)abstract
- Hammerstein systems are the series composition of a static nonlinear function and a linear dynamic system, In this work, we propose a nonparametric method for the identification of Hammerstein systems. We adopt a kernel-based approach to model the two components of the system. In particular, we model the nonlinear function and the impulse response of the linear block as Gaussian processes with suitable kernels. The kernels can be chosen to encode prior information about the nonlinear function and the system. Following the empirical Bayes approach, we estimate the posterior mean of the impulse response using estimates of the nonlinear function, of the hyperparameters, and of the noise variance. These estimates are found by maximizing the marginal likelihood of the data. This maximization problem is solved using an iterative scheme based on the expectation-conditional maximization, which is a variation of the standard expectation maximization method for solving maximum-likelihood problems. We show the effectiveness of the proposed identification scheme in some simulation experiments.
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| 8. |
- Risuleo, Riccardo Sven, 1986-, et al.
(författare)
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Approximate inference of nonparametric Hammerstein models
- 2017
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Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 50:1, s. 8333-8338
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Tidskriftsartikel (refereegranskat)abstract
- We propose a method for nonparametric identification of Hammerstein models with Gaussian-process models for the impulse response of the linear block and for the input nonlinearity. Interpreting the Gaussian-processes as prior distributions, we can estimate the unknowns using the posterior means given the data. To estimate the hyperparameters we set up an iterative scheme, reminiscent of the expectation-maximization method, where the posterior expectation of the complete likelihood is iteratively maximized. In the Hammerstein case, the posterior density is intractable because, in general, it does not admit a closed form expression. In this work, we propose two approximation approaches to estimate the posterior mean. In the first, we make a particle approximation of the posterior using Markov Chain Monte Carlo. In the second, we use a variational Bayes approach with a mean-field hypothesis. We validate the proposed methods on synthetic datasets of Hammerstein systems.
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| 9. |
- Risuleo, Riccardo Sven, 1986-, et al.
(författare)
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Approximate Maximum-likelihood Identification of Linear Systems from Quantized Measurements⁎
- 2018
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Ingår i: IFAC-PapersOnLine. - : Elsevier B.V.. - 2405-8963. ; 51:15, s. 724-729
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Tidskriftsartikel (refereegranskat)abstract
- We analyze likelihood-based identification of systems that are linear in the parameters from quantized output data; in particular, we propose a method to find approximate maximum-likelihood and maximum-a-posteriori solutions. The method consists of appropriate least-squares projections of the middle point of the active quantization intervals. We show that this approximation maximizes a variational approximation of the likelihood and we provide an upper bound for the approximation error. In a simulation study, we compare the proposed method with the true maximum-likelihood estimate of a finite impulse response model.
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| 10. |
- Risuleo, Riccardo Sven, 1986-
(författare)
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Bayesian learning of structured dynamical systems
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis, we propose some Bayesian approaches to the identificationof structured dynamical systems. In particular, we consider block-orientedmodels in which a complex system is built starting from simple linear andnonlinear building blocks. Each building block has a Gaussian-process modelthat can be used to include prior information into the learning problem.The learning is then guided by Bayes’ theorem. In particular, we use anempirical Bayes approach to perform the identification of models with hyper-parameters. As the models considered in this thesis are, in general, intractable,we propose several approximation methods based on variational Bayes andMarkov-chain Monte Carlo sampling. To estimate the hyperpameters, wepropose iterative algorithms based on variational expectation maximizationand stochastic-approximation expectation maximization.The main contribution of the thesis is developed in Part II. Here, we firststudy uncertain-input systems and Wiener systems as the typical Gaussian-process models of two-block cascades. In addition, we propose a robust ap-proach for uncertain-input systems with outliers in the measurements. Then,we proceed considering more complex structures such as acyclic networksof linear dynamical systems, feedback interconnections of linear systems,and three-block nonlinear structures such as the Wiener-Hammerstein andHammerstein-Wiener cascades. Finally, we consider some problems relatedto quantized measurements: we propose an approximate estimator and weprovide a rigorous analysis of the statistical properties of quantization noise.All the models and methods are discussed in detail and accompanied byalgorithms and implementation details. The proposed techniques are shownin several simulation examples.
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