| 1. |
- Abdalmoaty, Mohamed Rasheed, 1986-, et al.
(författare)
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A Simulated Maximum Likelihood Method for Estimation of Stochastic Wiener Systems
- 2016
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Ingår i: 2016 IEEE 55th Conference on Decision and Control (CDC). - : IEEE. - 9781509018376 - 9781509018444 - 9781509018383 ; , s. 3060-3065
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Konferensbidrag (refereegranskat)abstract
- This paper introduces a simulation-based method for maximum likelihood estimation of stochastic Wienersystems. It is well known that the likelihood function ofthe observed outputs for the general class of stochasticWiener systems is analytically intractable. However, when the distributions of the process disturbance and the measurement noise are available, the likelihood can be approximated byrunning a Monte-Carlo simulation on the model. We suggest the use of Laplace importance sampling techniques for the likelihood approximation. The algorithm is tested on a simple first order linear example which is excited only by the process disturbance. Further, we demonstrate the algorithm on an FIR system with cubic nonlinearity. The performance of the algorithm is compared to the maximum likelihood method and other recent techniques.
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| 2. |
- Annergren, Mariette, 1982-
(författare)
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Application-Oriented Input Design and Optimization Methods Involving ADMM
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis is divided into two main parts. The first part considers application-oriented input design, specifically for model predictive control (MPC). The second part considers alternating direction method of multipliers (ADMM) for ℓ1 regularized optimization problems and primal-dual interior-point methods.The theory of system identification provides methods for estimating models of dynamical systems from experimental data. This thesis is focused on identifying models used for control, with special attention to MPC. The objective is to minimize the cost of the identification experiment while guaranteeing, with high probability, that the obtained model gives an acceptable control performance. We use application-oriented input design to find such a model. We present a general procedure of implementing application-oriented input design to unknown, possibly nonlinear, systems controlled using MPC. The practical aspects of application-oriented input design are addressed and the method is tested in an experimental study.In addition, a MATLAB-based toolbox for solving application-oriented input design problems is presented. The purpose of the toolbox is threefold: it is used in research; it facilitates communication of research results; it helps an engineer to use application-oriented input design.Several important problems in science can be formulated as convex optimization problems. As such, there exist very efficient algorithms for finding the solutions. We are interested in methods that can handle optimization problems with a very large number of variables. ADMM is a method capable of handling such problems. We derive a scalable and efficient algorithm based on ADMM for two ℓ1 regularized optimization problems: ℓ1 mean and covariance filtering, and ℓ1 regularized MPC. The former occurs in signal processing and the latter is a specific type of model based control.We are also interested in optimization problems with certain structural limitations. These limitations inhibit the use of a central computational unit to solve the problems. We derive a distributed method for solving them instead. The method is a primal-dual interior-point method that uses ADMM to distribute all the calculations necessary to solve the optimization problem at hand.
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| 3. |
- Burström, Lage, et al.
(författare)
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Daily text messages used as a method for assessing low back pain among workers
- 2016
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Ingår i: Journal of Clinical Epidemiology. - : Elsevier BV. - 0895-4356 .- 1878-5921. ; :70, s. 45-51
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVES: To evaluate a method for collecting data concerning low back pain (LBP) using daily text messages and to characterize the reported LBP in terms of intensity, variability, and episodes.STUDY DESIGN AND SETTING: We conducted a cohort study of LBP among workers used by a mining company. The participants were asked to answer the question "How much pain have you had in your lower back in the last 24 hours on a scale from 0 to 10, where 0 = no pain and 10 = the worst pain imaginable" once a day for 5 weeks, with this process being repeated 6 months later.RESULTS: A total of 121 workers participated in the first period of data collection, and 108 participated in the second period. The daily response rate was 93% for both periods, and cluster analysis was shown to be a feasible statistical method for clustering LBP into subgroups of low, medium, and high pain. The daily text messages method also worked well for assessing the episodic nature of LBP.CONCLUSION: We have demonstrated a method for repeatedly measuring of LBP using daily text messages. The data permitted clustering into subgroups and could be used to define episodes of LBP.
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| 4. |
- Everitt, Niklas, et al.
(författare)
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Identification of modules in dynamic networks : An empirical Bayes approach
- 2016
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Ingår i: 2016 IEEE 55th Conference on Decision and Control, CDC 2016. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509018376 ; , s. 4612-4617
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Konferensbidrag (refereegranskat)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.
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| 5. |
- Galrinho, Miguel, et al.
(författare)
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A Weighted Least Squares Method for Estimation of Unstable Systems
- 2016
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Ingår i: 2016 IEEE 55th Conference on Decision and Control, CDC 2016. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509018376 ; , s. 341-346
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Konferensbidrag (refereegranskat)abstract
- Estimating unstable systems typically requires additional system identification techniques. In this paper, we consider the weighted null-space fitting method, a three step method that is asymptotically efficient for stable systems. This method first estimates a high order ARX model and then reduces it to a structured model with lower variance using weighted least squares. However, with unstable systems, the method cannot be used to simultaneously estimate the stable and unstable poles. To solve this, we observe that the unstable poles can be estimated from the high order ARX model with relative high accuracy, and use this as an estimate for the unstable poles of the model of interest. Then, the remaining parameters in this model can be estimated by weighted least squares. Because the complete set of parameters is not estimated jointly, asymptotic efficiency is lost. Nevertheless, a simulation study shows good performance.
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| 6. |
- Galrinho, Miguel
(författare)
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Least Squares Methods for System Identification of Structured Models
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The purpose of system identification is to build mathematical models for dynamical systems from experimental data. With the current increase in complexity of engineering systems, an important challenge is to develop accurate and computationally efficient algorithms.For estimation of parametric models, the prediction error method (PEM) is a benchmark in the field. When the noise is Gaussian and a quadratic cost function is used, PEM provides asymptotically efficient estimates if the model orders are correct. A disadvantage with PEM is that, in general, it requires minimizing a non-convex function. Alternative methods are then needed to provide initialization points for the optimization. Two important classes of such methods are subspace and instrumental variables.Other methods, such as Steiglitz-McBride, use iterative least squares to avoid the non-convexity of PEM. This thesis focuses on this class of methods, with the purpose of addressing common limitations in existing algorithms and suggesting more accurate and computationally efficient ones. In particular, the proposed methods first estimate a high order non-parametric model and then reduce this estimate to a model of lower order by iteratively applying least squares.Two methods are proposed. First, the weighted null-space fitting (WNSF) uses iterative weighted least squares to reduce the high order model to a parametric model of interest. Second, the model order reduction Steiglitz-McBride (MORSM) uses pre-filtering and Steiglitz-McBride to estimate a parametric model of the plant. The asymptotic properties of the methods are studied, which show that one iteration provides asymptotically efficient estimates. We also discuss two extensions for this type of methods: transient estimation and estimation of unstable systems.Simulation studies provide promising results regarding accuracy and convergence properties in comparison with PEM.
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| 7. |
- Hägg, Per, et al.
(författare)
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The Transient Impulse Response Modeling Method for Non-parametric System Identication
- 2016
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Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 68, s. 314-328
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Tidskriftsartikel (refereegranskat)abstract
- A method for the nonparametric estimation of the Frequency Response Function (FRF) was introduced in [5] and latercalled Transient Impulse Response Modeling Method (trimm). We present here a slightly improved version of the originalmethod and, more importantly, we thoroughly analyze the method in terms of bias and variance errors. This analysis leads toguidelines for the choice of the design parameters of the trimm method. Our theoretical expressions for the bias and varianceerrors are validated by simulations which, at the same time, highlight the eect of the design parameters on the performanceof the method.
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| 8. |
- Larsson, Christian A., et al.
(författare)
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An application-oriented approach to dual control with excitation for closed-loop identification
- 2016
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Ingår i: European Journal of Control. - : Elsevier. - 0947-3580 .- 1435-5671. ; 29, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- Identification of systems operating in closed loop is an important problem in industrial applications, where model-based control is used to an increasing extent. For model-based controllers, plant changes over time eventually result in a mismatch between the dynamics of any initial model in the controller and the actual plant dynamics. When the mismatch becomes too large, control performance suffers and it becomes necessary to re-identify the plant to restore performance. Often the available data are not informative enough when the identification is performed in closed loop and extra excitation needs to be injected. This paper considers the problem of generating such excitation with the least possible disruption to the normal operations of the plant. The methods explicitly take time domain constraints into account. The formulation leads to optimal control problems which are in general very difficult optimization problems. Computationally tractable solutions based on Markov decision processes and model predictive control are presented. The performance of the suggested algorithms is illustrated in two simulation examples comparing the novel methods and algorithms available in the literature.
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| 9. |
- Larsson, Christian A., et al.
(författare)
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Generation of signals with specified second-order properties for constrained systems
- 2016
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Ingår i: International journal of adaptive control and signal processing (Print). - : Wiley. - 0890-6327 .- 1099-1115. ; 30:3, s. 456-472
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Tidskriftsartikel (refereegranskat)abstract
- This contribution considers the problem of realizing an input signal with a desired autocorrelation sequence satisfying both input and output constraints for the system it is to be applied to. This is an important problem in system identification, firstly, because the quality and accuracy of the identified model are highly dependent on the excitation signal used during the experiment and secondly, because on real processes, it is often important to constrain the input and output of the process because of actuator saturation and safety considerations. The signal generation is formulated as a model predictive controller with probabilistic constraints to make the algorithm robust to model uncertainties and process noise. The corresponding optimization problem is then solved with tools from scenario-based stochastic optimization. To reduce the model uncertainties, the method is made adaptive where a new model of the system and its uncertainties are reidentified. The algorithm is successfully applied to a simulation example and in a practical experiment for the identification of a quadruple tank lab process.
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| 10. |
- Li, Kezhi, et al.
(författare)
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Piecewise sparse signal recovery via piecewise orthogonal matching pursuit
- 2016
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Ingår i: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781479999880 ; , s. 4608-4612
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Konferensbidrag (refereegranskat)abstract
- In this paper, we consider the recovery of piecewise sparse signals from incomplete noisy measurements via a greedy algorithm. Here piecewise sparse means that the signal can be approximated in certain domain with known number of nonzero entries in each piece/segment. This paper makes a two-fold contribution to this problem: 1) formulating a piecewise sparse model in the framework of compressed sensing and providing the theoretical analysis of corresponding sensing matrices; 2) developing a greedy algorithm called piecewise orthogonal matching pursuit (POMP) for the recovery of piecewise sparse signals. Experimental simulations verify the effectiveness of the proposed algorithms.
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