SwePub - search: WFRF:(Bottegal Giulio)

Enumeration	Reference	Cover	Find
1.	Bottegal, Giulio, et al. (author) A new kernel-based approach to system identification with quantized output data 2017 In: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 85, s. 145-152 Journal article (peer-reviewed)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.
2.	Bottegal, Giulio, et al. (author) Analysis and identification of complex stochastic systems admitting a flocking structure 2014 In: IFAC Proceedings Volumes (IFAC-PapersOnline). - : Elsevier BV. - 1474-6670. - 9783902823625 ; , s. 2323-2328 Conference paper (peer-reviewed)abstract We discuss a new modeling paradigm for large dimensional aggregates of stochastic systems by Generalized Factor Analysis (GFA) models. These models describe the data as the sum of a flocking plus an uncorrelated idiosyncratic component. The flocking component describes a sort of collective orderly motion which admits a much simpler mathematical description than the whole ensemble while the idiosyncratic component describes weakly correlated noise. The extraction of the dynamic flocking component is discussed for time-stationary systems.
3.	Bottegal, Giulio, et al. (author) Bayesian kernel-based system identification with quantized output data 2015 In: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 48:28, s. 455-460 Journal article (peer-reviewed)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.
4.	Bottegal, Giulio, et al. (author) Blind system identification using kernel-based methods 2015 In: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. Conference paper (peer-reviewed)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.
5.	Bottegal, Giulio, et al. (author) Modeling Complex Systems by Generalized Factor Analysis 2015 In: IEEE Transactions on Automatic Control. - 0018-9286 .- 1558-2523. ; 60:3, s. 759-774 Journal article (peer-reviewed)abstract We propose a new modeling paradigm for large dimensional aggregates of stochastic systems by Generalized Factor Analysis (GFA) models. These models describe the data as the sum of a flocking plus an uncorrelated idiosyncratic component. The flocking component describes a sort of collective orderly motion which admits a much simpler mathematical description than the whole ensemble while the idiosyncratic component describes weakly correlated noise. We first discuss static GFA representations and characterize in a rigorous way the properties of the two components. The extraction of the dynamic flocking component is discussed for time-stationary linear systems and for a simple classes of separable random fields.
6.	Bottegal, Giulio, 1984-, et al. (author) Modeling random flocks through Generalized Factor Analysis 2013 In: 2013 European Control Conference, ECC 2013. - : EUCA. - 9783952417348 ; , s. 2421-2426 Conference paper (peer-reviewed)abstract In this paper, we study modeling and identification of stochastic systems by Generalized Factor Analysis models. Although this class of models was originally introduced for econometric purposes, we present some possible applications of engineering interest. In particular, we show that there is a natural connection between Generalized Factor Analysis models and multi-agents systems. The common factor component of the model has an interpretation as a flocking component of the system behavior.
7.	Bottegal, Giulio, 1984-, et al. (author) On the variance of identified SIMO systems with spatially correlated output noise 2014 In: Proceedings of the IEEE Conference on Decision and Control. - : IEEE conference proceedings. ; , s. 2636-2641 Conference paper (peer-reviewed)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.
8.	Bottegal, Giulio, et al. (author) Outlier robust kernel-based system identification using l1-Laplace techniques 2015 In: 2015 54th IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2109-2114 Conference paper (peer-reviewed)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.
9.	Bottegal, Giulio, et al. (author) Outlier robust system identification : A Bayesian kernel-based approach 2014 In: IFAC Proceedings Volumes (IFAC-PapersOnline). - : IFAC Papers Online. - 9783902823625 ; , s. 1073-1078 Conference paper (peer-reviewed)abstract In this paper, we propose an outlier-robust regularized kernel-based method for linear system identification. The unknown impulse response is modeled 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. To build robustness to outliers, we model the measurement noise as realizations of independent Laplacian random variables. The identification problem is cast in a Bayesian framework, and solved by a new Markov Chain Monte Carlo (MCMC) scheme. In particular, exploiting the representation of the Laplacian random variables as scale mixtures of Gaussians, we 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.
10.	Bottegal, Giulio, et al. (author) Robust EM kernel-based methods for linear system identification 2016 In: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 67, s. 114-126 Journal article (peer-reviewed)abstract Recent developments in system identification have brought attention to regularized kernel-based methods. This type of approach has been proven to compare favorably with classic parametric methods. However, current formulations are not robust with respect to outliers. In this paper, we introduce a novel method to robustify kernel-based system identification methods. To this end, we model the output measurement noise using random variables with heavy-tailed probability density functions (pdfs), focusing on the Laplacian and the Student's t distributions. Exploiting the representation of these pdfs as scale mixtures of Gaussians, we cast our system identification problem into a Gaussian process regression framework, which requires estimating a number of hyperparameters of the data size order. To overcome this difficulty, we design a new maximum a posteriori (MAP) estimator of the hyperparameters, and solve the related optimization problem with a novel iterative scheme based on the Expectation-Maximization (EM) method. In the presence of outliers, tests on simulated data and on a real system show a substantial performance improvement compared to currently used kernel-based methods for linear system identification. (C) 2016 Elsevier Ltd. All rights reserved.
11.	Ebadat, Afrooz, et al. (author) Blind identification strategies for room occupancy estimation 2015 In: 2015 European Control Conference (ECC). - Piscataway, NJ : IEEE Communications Society. - 9783952426937 ; , s. 1315-1320 Conference paper (peer-reviewed)abstract We propose and test on real data a two-tier estimation strategy for inferring occupancy levels from measurements of CO2 concentration and temperature levels. The first tier is a blind identification step, based either on a frequentist Maximum Likelihood method, implemented using non-linear optimization, or on a Bayesian marginal likelihood method, implemented using a dedicated Expectation-Maximization algorithm. The second tier resolves the ambiguity of the unknown multiplicative factor, and returns the final estimate of the occupancy levels. The overall procedure addresses some practical issues of existing occupancy estimation strategies. More specifically, first it does not require the installation of special hardware, since it uses measurements that are typically available in many buildings. Second, it does not require apriori knowledge on the physical parameters of the building, since it performs system identification steps. Third, it does not require pilot data containing measured real occupancy patterns (i.e., physically counting people for some periods, a typically expensive and time consuming step), since the identification steps are blind.
12.	Ebadat, Afrooz, et al. (author) Estimation of building occupancy levels through environmental signals deconvolution 2013 In: Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings. - New York, NY, USA : ACM. - 9781450324311 Conference paper (peer-reviewed)abstract We address the problem of estimating the occupancy levelsin rooms using the information available in standardHVAC systems. Instead of employing dedicated devices, weexploit the significant statistical correlations between the occupancylevels and the CO2 concentration, room temperature,and ventilation actuation signals in order to identify adynamic model. The building occupancy estimation problemis formulated as a regularized deconvolution problem, wherethe estimated occupancy is the input that, when injected intothe identified model, best explains the currently measuredCO2 levels. Since occupancy levels are piecewise constant,the zero norm of occupancy is plugged into the cost functionto penalize non-piecewise constant inputs. The problemthen is seen as a particular case of fused-lasso estimator byrelaxing the zero norm into the `1 norm. We propose bothonline and offline estimators; the latter is shown to performfavorably compared to other data-based building occupancyestimators. Results on a real testbed show that the MSE ofthe proposed scheme, trained on a one-week-long dataset, is half the MSE of equivalent Neural Network (NN) or SupportVector Machine (SVM) estimation strategies.
13.	Ebadat, Afrooz, et al. (author) Multi-room occupancy estimation through adaptive gray-box models 2015 In: IEEE 54th Annual Conference on Decision and Control (CDC). - Piscataway, NJ : IEEE Communications Society. - 9781479978861 ; , s. 3705-3711, s. 3705-3711 Conference paper (peer-reviewed)abstract We consider the problem of estimating the occupancy level in buildings using indirect information such as CO2 concentrations and ventilation levels. We assume that one of the rooms is temporarily equipped with a device measuring the occupancy. Using the collected data, we identify a gray-box model whose parameters carry information about the structural characteristics of the room. Exploiting the knowledge of the same type of structural characteristics of the other rooms in the building, we adjust the gray-box model to capture the CO2 dynamics of the other rooms. Then the occupancy estimators are designed using a regularized deconvolution approach which aims at estimating the occupancy pattern that best explains the observed CO2 dynamics. We evaluate the proposed scheme through extensive simulation using a commercial software tool, IDA-ICE, for dynamic building simulation.
14.	Ebadat, Afrooz, et al. (author) Regularized Deconvolution-Based Approaches for Estimating Room Occupancies 2015 In: IEEE Transactions on Automation Science and Engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1545-5955 .- 1558-3783. ; 12:4, s. 1157-1168 Journal article (peer-reviewed)abstract We address the problem of estimating the number of people in a room using information available in standard HVAC systems. We propose an estimation scheme based on two phases. In the first phase, we assume the availability of pilot data and identify a model for the dynamic relations occurring between occupancy levels, CO2 concentration and room temperature. In the second phase, we make use of the identified model to formulate the occupancy estimation task as a deconvolution problem. In particular, we aim at obtaining an estimated occupancy pattern by trading off between adherence to the current measurements and regularity of the pattern. To achieve this goal, we employ a special instance of the so-called fused lasso estimator, which promotes piecewise constant estimates by including an l(1) norm-dependent term in the associated cost function. We extend the proposed estimator to include different sources of information, such as actuation of the ventilation system and door opening/closing events. We also provide conditions under which the occupancy estimator provides correct estimates within a guaranteed probability. We test the estimator running experiments on a real testbed, in order to compare it with other occupancy estimation techniques and assess the value of having additional information sources.
15.	Everitt, Niklas, et al. (author) An empirical Bayes approach to identification of modules in dynamic networks 2018 In: Automatica. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0005-1098 .- 1873-2836. ; 91, s. 144-151 Journal article (peer-reviewed)abstract We present a new method of identifying a specific module in a dynamic network, possibly with feedback loops. Assuming known topology, we express the dynamics by an acyclic network composed of two blocks where the first block accounts for the relation between the known reference signals and the input to the target module, while the second block contains the target module. Using an empirical Bayes approach, we model the first block as a Gaussian vector with covariance matrix (kernel) given by the recently introduced stable spline kernel. The parameters of the target module are estimated by solving a marginal likelihood problem with a novel iterative scheme based on the Expectation-Maximization algorithm. Additionally, we extend the method to include additional measurements downstream of the target module. Using Markov Chain Monte Carlo techniques, it is shown that the same iterative scheme can solve also this formulation. Numerical experiments illustrate the effectiveness of the proposed methods.
16.	Everitt, Niklas, et al. (author) Identification of modules in dynamic networks : An empirical Bayes approach 2016 In: 2016 IEEE 55th Conference on Decision and Control, CDC 2016. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509018376 ; , s. 4612-4617 Conference paper (peer-reviewed)abstract We address the problem of identifying a specific module in a dynamic network, assuming known topology. We express the dynamics by an acyclic network composed of two blocks where the first block accounts for the relation between the known reference signals and the input to the target module, while the second block contains the target module. Using an empirical Bayes approach, we model the first block as a Gaussian vector with covariance matrix (kernel) given by the recently introduced stable spline kernel. The parameters of the target module are estimated by solving a marginal likelihood problem with a novel iterative scheme based on the Expectation Maximization algorithm. Numerical experiments illustrate the effectiveness of the proposed method.
17.	Everitt, Niklas, et al. (author) On the Effect of Noise Correlation in Parameter Identification of SIMO Systems 2015 In: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 48:28, s. 326-331 Journal article (peer-reviewed)abstract The accuracy of identified linear time-invariant single-input multi-output (SIMO) models can be improved when the disturbances affecting the output measurements are spatially correlated. Given a linear parametrization of the modules composing the SIMO structure, we show that the correlation structure of the noise sources and the model structure of the othe modules determine the variance of a parameter estimate. In particular we show that increasing the model order only increases the variance of other modules up to a point. We precisely characterize the variance error of the parameter estimates for finite model orders. We quantify the effect of noise correlation structure, model structure and signal spectra.
18.	Everitt, Niklas, et al. (author) On the Variance Analysis of identified Linear MIMO Models 2015 In: IEEE Explore. - : Institute of Electrical and Electronics Engineers (IEEE). Conference paper (peer-reviewed)abstract We study the accuracy of identified linear time-invariant multi-input multi-output (MIMO) systems. Under a stochastic framework, we quantify the effect of the spatial correlation and choice of model structure on the covariance matrix of the transfer function estimates. In particular, it is shown how the variance of a transfer function estimate depends on signal properties and model orders of other modules composing the MIMO system.
19.	Everitt, Niklas, 1987-, et al. (author) Variance Analysis of Linear SIMO Models with Spatially Correlated Noise Other publication (other academic/artistic)abstract Substantial improvement in accuracy of identied linear time-invariant single-input multi-output (SIMO) dynamical models ispossible when the disturbances aecting the output measurements are spatially correlated. Using an orthogonal representation for the modules composing the SIMO structure, in this paper we show that the variance of a parameter estimate of a module is dependent on the model structure of the other modules, and the correlation structure of the disturbances. In addition, we quantify the variance-error for the parameter estimates for finite model orders, where the effect of noise correlation structure, model structure and signal spectra are visible. From these results, we derive the noise correlation structure under which the mentioned model parameterization gives the lowest variance, when one module is identied using less parameters than the other modules.
20.	Everitt, Niklas, et al. (author) Variance analysis of linear SIMO models with spatially correlated noise 2017 In: Automatica. - : Elsevier. - 0005-1098. ; 77, s. 68-81 Journal article (peer-reviewed)abstract In this paper we address the identification of linear time-invariant single-input multi-output (SIMO) systems. In particular, we assess the performance of the prediction error method by quantifying the variance of the parameter estimates. Using an orthonormal representation for the modules composing the SIMO structure, we show that the parameter estimate of a module depends on the model structure of the other modules, and on the correlation structure of the output disturbances. We provide novel results which quantify the variance-error of the parameter estimates for finite model orders, where the effects of noise correlation structure, model structure and input spectrum are visible. In particular, we show that a sensor does not increase the accuracy of a module if common dynamics have to be estimated. When a module is identified using less parameters than the other modules, we derive the noise correlation structure that gives the minimum total variance. The implications of our results are illustrated through numerical examples and simulations.
21.	Risuleo, Riccardo Sven, 1986-, et al. (author) A benchmark for data-based office modeling: challenges related to CO2 dynamics 2015 In: IFAC-PapersOnLine. - : IFAC Papers Online. - 2405-8963. ; , s. 1256-1261 Conference paper (peer-reviewed)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.
22.	Risuleo, Riccardo Sven, 1986-, et al. (author) A kernel-based approach to Hammerstein system identication 2015 In: IFAC-PapersOnLine. - : IFAC Papers Online. - 2405-8963. ; , s. 1011-1016 Conference paper (peer-reviewed)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.
23.	Risuleo, Riccardo Sven, 1986-, et al. (author) A new kernel-based approach to overparameterized Hammerstein system identification 2015 In: 2015 54th IEEE Conference on Decision and Control (CDC). - : IEEE conference proceedings. - 9781479978847 ; , s. 115-120 Conference paper (peer-reviewed)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.
24.	Risuleo, Riccardo Sven, 1986-, et al. (author) A nonparametric kernel-based approach to Hammerstein system identification 2017 In: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 85, s. 234-247 Journal article (peer-reviewed)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.
25.	Risuleo, Riccardo Sven, 1986-, et al. (author) Approximate Maximum-likelihood Identification of Linear Systems from Quantized Measurements⁎ 2018 In: IFAC-PapersOnLine. - : Elsevier B.V.. - 2405-8963. ; 51:15, s. 724-729 Journal article (peer-reviewed)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.
26.	Risuleo, Riccardo Sven, 1986-, et al. (author) Identification of linear models from quantized data : A midpoint-projection approach 2020 In: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286 .- 1558-2523. ; 65:7, s. 2801-2813 Journal article (peer-reviewed)abstract In this article, we consider the identification of linear models from quantized output data. We develop a variational approximation of the likelihood function, which allows us to find variationally optimal approximations of the maximum-likelihood and maximum a posteriori estimates. We show that these estimates are obtained by projecting the midpoint in the quantization interval of each output measurement onto the column space of the input regression matrix. Interpreting the quantized output as a random variable, we derive its moments for generic noise distributions. For the case of Gaussian noise and Gaussian independent identically distributed input, we give an analytical characterization of the bias, which we use to build a bias-compensation scheme that leads to consistent estimates.
27.	Risuleo, Riccardo Sven, 1986-, et al. (author) Kernel-based system identification from noisy and incomplete intput-output data 2016 In: 2016 IEEE 55th Conference on Decision and Control, CDC 2016. - : IEEE conference proceedings. - 9781509018376 ; , s. 2061-2066 Conference paper (peer-reviewed)abstract In this contribution, we propose a kernel-based method for the identification of linear systems from noisy and incomplete input-output datasets. We model the impulse response of the system as a Gaussian process whose covariance matrix is given by the recently introduced stable spline kernel. We adopt an empirical Bayes approach to estimate the posterior distribution of the impulse response given the data. The noiseless and missing data samples, together with the kernel hyperparameters, are estimated maximizing the joint marginal likelihood of the input and output measurements. To compute the marginal-likelihood maximizer, we build a solution scheme based on the Expectation-Maximization method. Simulations on a benchmark dataset show the effectiveness of the method.
28.	Risuleo, Riccardo Sven, 1986-, et al. (author) Modeling and identification of uncertain-input systems 2019 In: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 105, s. 130-141 Journal article (peer-reviewed)abstract We present a new class of models, called uncertain-input models, that allows us to treat system-identification problems in which a linear system is subject to a partially unknown input signal. To encode prior information about the input or the linear system, we use Gaussian-process models. We estimate the model from data using the empirical Bayes approach: the hyperparameters that characterize the Gaussian-process models are estimated from the marginal likelihood of the data. We propose an iterative algorithm to find the hyperparameters that relies on the EM method and results in decoupled update steps. Because in the uncertain-input setting neither the marginal likelihood nor the posterior distribution of the unknowns is tractable, we develop an approximation approach based on variational Bayes. As part of the contribution of the paper, we show that this model structure encompasses many classical problems in system identification such as Hammerstein models, blind system identification, and cascaded linear systems. This connection allows us to build a systematic procedure that applies effectively to all the aforementioned problems, as shown in the numerical simulations presented in the paper.
29.	Risuleo, Riccardo Sven, 1986-, et al. (author) On the estimation of initial conditions in kernel-based system identification 2015 In: 2015 54th IEEE Conference on Decision and Control (CDC). - : IEEE conference proceedings. - 9781479978847 ; , s. 1120-1125 Conference paper (peer-reviewed)abstract Recent developments in system identification have brought attention to regularized kernel-based methods, where, adopting the recently introduced stable spline kernel, prior information on the unknown process is enforced. This reduces the variance of the estimates and thus makes kernel-based methods particularly attractive when few input-output data samples are available. In such cases however, the influence of the system initial conditions may have a significant impact on the output dynamics. In this paper, we specifically address this point. We propose three methods that deal with the estimation of initial conditions using different types of information. The methods consist in various mixed maximum likelihood-a posteriori estimators which estimate the initial conditions and tune the hyperparameters characterizing the stable spline kernel. To solve the related optimization problems, we resort to the expectation-maximization method, showing that the solutions can be attained by iterating among simple update steps. Numerical experiments show the advantages, in terms of accuracy in reconstructing the system impulse response, of the proposed strategies, compared to other kernel-based schemes not accounting for the effect initial conditions.
30.	Zamani, Mohsen, et al. (author) On the properties of linear multirate systems with coprime output rates 2013 In: 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). - : IEEE conference proceedings. - 9781467357173 ; , s. 2734-2739 Conference paper (peer-reviewed)abstract This paper studies discrete-time linear systems with multirate outputs, assuming that two measured output streams are available at coprime rates. In the literature this type of system, which can be considered as periodic timevarying, is commonly studied in its blocked version, since the well-known techniques of analysis developed for linear timeinvariant systems can be used. In particular, we focus on some structural properties of the blocked systems and we prove that, under a generic setting i.e. for a generic choice of parameter matrices, the blocked systems are minimal when the underlying multirate system is defined using a minimal dimension system. Moreover, we focus on zeros of tall blocked systems i.e. blocked systems with more outputs than inputs. In particular, we study those cases where the associated system matrix attains fullcolumn rank. We exhibit situations where they generically have no finite nonzero zeros.
31.	Zamani, Mohsen, et al. (author) On the Zero-Freeness of Tall Multirate Linear Systems 2016 In: IEEE Transactions on Automatic Control. - : IEEE. - 0018-9286 .- 1558-2523. ; 61:11, s. 3606-3611 Journal article (peer-reviewed)abstract In this technical note, tall discrete-time linear systems with multirate outputs are studied. In particular, we focus on their zeros. In systems and control literature zeros of multirate systems are defined as those of their corresponding time-invariant systems obtained through blocking of the original multirate systems. We assume that blocked systems are tall, i.e., have more outputs than inputs. It is demonstrated that, for generic choice of the parameter matrices, linear systems with multirate outputs generically have no finite nonzero zeros. However, they may have zeros at the origin or at infinity depending on the choice of blocking delay and the input, state and output dimensions.

Skapa referenser, mejla, bekava och länka

Permalink

Träfflista för sökning "WFRF:(Bottegal Giulio) "

Refine your search

Year