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prediction error methods for limit cycle data

AUTOMATICA 2002年第10期38卷 1753-1760页

作者： Casas, RA Bitmead, RR Jacobson, CA Johnson, CR NxtWave Commun Langhorne PA 19047 USA Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92093 USA United Technol Res Ctr E Hartford CT 06108 USA Cornell Univ Sch Elect Engn Ithaca NY 14853 USA

prediction error methods are considered for identification of the forward linear dynamics of nonlinear feedback closed-loop systems which operate in a perturbed stable limit cycle. A model of the signals measured in a neighborhood of the limit cycle is presented and shown to satisfy a quasistationarity property. Quasistationarity is then used to prove that prediction error methods are both convergent and consistent for our data model. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： closed-loop identification limit cycle prediction error methods

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Linear prediction error methods for stochastic nonlinear models

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AUTOMATICA 2019年 105卷 49-63页

作者： Abdalmoaty, Mohamed Rasheed-Hilmy Hjalmarsson, Hakan KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Decis & Control Syst Malvinas Vag 10Floor 6 SE-10044 Stockholm Sweden

The estimation problem for stochastic parametric nonlinear dynamical models is recognized to be challenging. The main difficulty is the intractability of the likelihood function and the optimal one-step ahead predictor. In this paper, we present relatively simple prediction error methods based on non-stationary predictors that are linear in the outputs. They can be seen as extensions of the linear prediction error methods for the case where the hypothesized model is stochastic and nonlinear. The resulting estimators are defined by analytically tractable objective functions in several common cases. It is shown that, under certain identifiability and standard regularity conditions, the estimators are consistent and asymptotically normal. We discuss the relationship between the suggested estimators and those based on second-order equivalent models as well as the maximum likelihood method. The paper is concluded with a numerical simulation example as well as a real-data benchmark problem. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Parameter estimation System identification Stochastic systems Nonlinear models prediction error methods

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Dynamic network structure identification with prediction error methods - basic examples

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IFAC Proceedings Volumes 2012年第16期45卷 876-881页

作者： Arne G. Dankers Paul M.J. Van den Hof Peter S.C. Heuberger Xavier Bombois Delft Center for Systems and Control Delft University of Technology Mekelweg 2 2628 CD Delft The Netherlands Dept. of Electrical Engineering Eindhoven University of Technology P.O. Box 513 5600 MB Eindhoven The Netherlands

Modeling of dynamical properties of highly complex and interconnected systems becomes important in different fields of science. When identifying the structure and dynamics of a network of interconnected dynamical systems, including cause-effect relations, there is a tendency to use nonparametric or FIR models of the output error type. In this paper it is shown, and illustrated by some simple examples, that appropriate attention should be given to using flexible noise models, in order to allow consistent identification of the dynamics, while the use of external excitation/probing signals may reduce this need. It is a first step towards using prediction error identification tools to identify the structure of a network.

关键词： System identification Identifiability Network topologies Complex systems prediction error methods

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ON MULTISTEP prediction error methods FOR TIME-SERIES MODELS

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JOURNAL OF FORECASTING 1989年第4期8卷 357-368页

作者： STOICA, P NEHORAI, A YALE UNIV DEPT ELECT ENGNNEW HAVENCT 06520 YALE UNIV APPL MATH PROGRAMNEW HAVENCT 06520

Multistep prediction error methods for linear time series models are considered from both a theoretical and a practical standpoint. The emphasis is on autoregressive moving‐average (ARMA) models for which a multistep prediction error estimation method (PEM) is developed. The results of a Monte Carlo simulation study aimed at establishing the possible merits of the multistep PEM are presented.

关键词： ARMA models prediction error methods One‐step and multistep methods Gradient algorithms prediction performance assessment

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prediction error vs subspace methods in closed loop identification

Prediction error vs subspace methods in closed loop identifi...

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作者： Chiuso, Alessandro Picci, Giorgio Department of Information Engineering University of Padova 35131 Padova Italy

in this paper we investigate the role of the output predictor in subspace identification. We shall see that subspace identification of the predictor model can ideally yield consistent estimates regardless of the prese... 详细信息

ISBN: (纸本)008045108X

关键词： Closed loop identification prediction error methods Subspace identification

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prediction error VS SUBSPACE methods IN CLOSED LOOP IDENTIFICATION

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IFAC Proceedings Volumes 2005年第1期38卷 506-511页

作者： Alessandro Chiuso Giorgio Picci Department of Information Engineering University of Padova 35131 Padova Italy

In this paper we investigate the role of the output predictor in subspace identification. We shall see that subspace identification of the predictor model can ideally yield consistent estimates regardless of the prese... 详细信息

关键词： Subspace identification closed loop identification prediction error methods

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Control-oriented closed-loop identification and input design in a wireless power transfer system

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WIRELESS POWER TRANSFER 2025年 12卷

作者： Li, Shuaiqi Deng, Qijun Li, Zhifan Hu, Wenshan Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China

This study proposes a closed-loop identification scheme for a wireless power transfer (WPT) system that estimates the actual system model and uses it to design and update the controller. Due to factors such as coil misalignment, unknown loads, and capacitor aging, the initial controller may not be suitable for the actual WPT devices. Therefore, this paper focuses on estimating the model during closed-loop operation and updating the controller when performance requirements are not satisfied. To minimize disturbances to normal operation while achieving sufficient modeling accuracy for controller design, this study proposes a method for designing the input excitation signal to minimize output voltage fluctuations. Additionally, a robust controller design method based on the estimated model set is introduced to enhance closed-loop control performance. Experimental prototypes demonstrate the effectiveness of the identification and controller switching scheme.

关键词： Excitation signal prediction error methods Robust control System identification Wireless power transfer

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Assessment of prediction Uncertainty Quantification methods in Systems Biology

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第3期20卷 1725-1736页

作者： Villaverde, Alejandro F. Raimundez, Elba Hasenauer, Jan Banga, Julio R. CITMAga Santiago De Compostela 15782 Galicia Spain Univ Vigo Dept Syst & Control Engn Vigo 36310 Galicia Spain Univ Bonn D-53113 Bonn Germany Tech Univ Munich Ctr Math D-85748 Garching Germany Helmholtz Zentrum Munchen D-85764 Neuherberg Germany MBG CSIC Biol Mission Galicia Spanish Natl Res Council Computat Biol Lab Pontevedra 36143 Galicia Spain

Biological processes are often modelled using ordinary differential equations. The unknown parameters of these models are estimated by optimizing the fit of model simulation and experimental data. The resulting parameter estimates inevitably possess some degree of uncertainty. In practical applications it is important to quantify these parameter uncertainties as well as the resulting prediction uncertainty, which are uncertainties of potentially time-dependent model characteristics. Unfortunately, estimating prediction uncertainties accurately is nontrivial, due to the nonlinear dependence of model characteristics on parameters. While a number of numerical approaches have been proposed for this task, their strengths and weaknesses have not been systematically assessed yet. To fill this knowledge gap, we apply four state of the art methods for uncertainty quantification to four case studies of different computational complexities. This reveals the trade-offs between their applicability and their statistical interpretability. Our results provide guidelines for choosing the most appropriate technique for a given problem and applying it successfully.

关键词： Uncertainty Biological system modeling Mathematical models Predictive models Computational modeling Data models Uncertain systems Computational methods dynamic models nonlinear systems observability prediction error methods state estimation uncertainty

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A Comparison of methods for Quantifying prediction Uncertainty in Systems Biology 8th

A Comparison of Methods for Quantifying Prediction Uncertain...

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8th Conference on Foundations of Systems Biology in Engineering (FOBSE)

作者： Villaverde, Alejandro F. Raimundez, Elba Hasenauer, Jan Banga, Julio R. CSIC IIM Bioproc Engn Grp Vigo 36208 Galicia Spain Helmholtz Zentrum Munchen D-85764 Neuherberg Germany Tech Univ Munich Chair Math Modeling Biol Syst Ctr Math D-85748 Garching Germany Univ Bonn D-53113 Bonn Germany

The parameters of dynamical models of biological processes always possess some degree of uncertainty. This parameter uncertainty translates into an uncertainty of model predictions. The trajectories of unmeasured state variables are examples of such predictions. Quantifying the uncertainty associated with a given prediction is an important problem for model developers and users. However, the nonlinearity and complexity of most dynamical models renders it nontrivial. Here, we evaluate three state-of-the-art approaches for prediction uncertainty quantification using two models of different sizes and computational complexities. We discuss the trade-offs between applicability and statistical interpretability of the different methods, and provide guidelines for their application. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Computational methods Dynamic models Nonlinear systems Observability prediction error methods State estimation Uncertainty

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A Comparison of methods for Quantifying prediction Uncertainty in Systems Biology ⁎

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IFAC-PapersOnLine 2019年第26期52卷 45-51页

作者： Alejandro F. Villaverde Elba Raimúndez Jan Hasenauer Julio R. Banga Bioprocess Engineering Group IIM-CSIC Vigo 36208 Galicia Spain Helmholtz Zentrum München 85764 Neuherberg Germany Center for Mathematics Chair of Mathematical Modeling of Biological Systems Technische Universitt München Garching 85748 Germany Universität Bonn 53113 Bonn Germany

关键词： Computational methods Dynamic models Nonlinear systems Observability prediction error methods State estimation Uncertainty

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