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  • 1.
    Hagenblad, Anna
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Identification of Wiener Models1998In: Proceedings of the First Conference on Computer Science and Systems Engineering, 1998, Vol. 1, p. 175-183Conference paper (Other academic)
    Abstract [en]

    The identification task consists of making a model of a system from measured input and output signals. Wiener models consist of a linear dynamic system, followed by a static nonlinearity. We derive an algorithm to calculate the maximum likelihood estimate of the model for this class of systems. We describe an implementation in some detail and show simulation results where a test system is successfully identified from data.

  • 2.
    Hagenblad, Anna
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Identification of Wiener Models1998Report (Other academic)
    Abstract [en]

    The identification task consists of making a model of a system from measured input and output signals. Wiener models consist of a linear dynamic system, followed by a static nonlinearity. We derive an algorithm to calculate the maximum likelihood estimate of the model for this class of systems. We describe an implementation in some detail and show simulation results where a test system is successfully identified from data.

  • 3.
    Hagenblad, Anna
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Identifiering av Wienermodeller1998In: Proceedings of Reglermöte 1998, 1998Conference paper (Other academic)
    Abstract [sv]

    En Wienermodell är en olinjär struktur som består av ett linjärt dynamiskt system, följt av en dynamisk olinjäritet. Vi presenterar en metod för identifiering av Wienermodeller, genom numerisk sökning efter maximum likelihoodskattningen av parametrarna. För att undvika problem med lokala minima föreslås en initialisering baserad på en minsta kvadratskattning.

  • 4.
    Hagenblad, Anna
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Identifiering av Wienermodeller1998Report (Other academic)
    Abstract [en]

    En Wienermodell är en olinjär struktur som består av ett linjärt dynamiskt system, följt av en dynamisk olinjäritet. Vi presenterar en metod för identifiering av Wienermodeller, genom numerisk sökning efter maximum likelihoodskattningen av parametrarna. För att undvika problem med lokala minima föreslås en initialisering baserad på en minsta kvadratskattning.

  • 5.
    Hagenblad, Anna
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Inconsistency of an Approximate Prediction Error Method for Wiener Model Identification2000Report (Other academic)
    Abstract [en]

    A Wiener model consists of a linear dynamic block followed by with a nonlinear static block. When identifying the parameters of such a system, the Prediction Error Method (PEM) can be used. Depending on how noise enters the system, the predictor can be difficult to express, and an approximate predictor may be interesting. The estimate obtained from using this approximate predictor is however not always consistent. In this report we investigate this inconsistency.

  • 6.
    Hagenblad, Anna
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Initialization and Model Reduction for Wiener Model Identification1999In: Proceedings of the 7th Mediterranean Conference on Control and Automation, 1999Conference paper (Refereed)
    Abstract [en]

    The identification of nonlinear systems by the minimization of a prediction error criterion suffers from the problem of local minima. To get a reliable estimate we need good initial values for the parameters. In this paper we discuss the class of nonlinear Wiener models, consisting of a linear dynamic system followed by a static nonlinearity. By selecting a parameterization where the parameters enter linearly in the error, we can obtain an initial estimate of the model via linear regression. An example shows that this approach may be preferential to trying to estimate the linear system directly form input-output data, if the input is not Gaussian. We discuss some of the users choices and how the linear regression initial estimate can be converted to a desired model structure to use in the prediction error criterion minimization. The method is also applied to experimental data.

  • 7.
    Hagenblad, Anna
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Initialization and model reduction for Wiener model identification1999Report (Other academic)
    Abstract [en]

    The identification of nonlinear systems by the minimization of a predictionerror criterion suffers from the problem of local minima. To get a reliableestimate we need good initial values for the parameters. In this paper wediscuss the class of nonlinear Wiener models, consisting of a linear dynamicsystem followed by a static nonlinearity. By selecting a parameterizationwhere the parameters enter linearly in the error, we can obtain an initialestimate of the model via linear regression. An example shows that thisapproach may be preferential to trying to estimate the linear system directlyform input-output data, if the input is not Gaussian. We discuss some of theusers choices and how the linear regression initial estimate can be convertedto a desired model structure to use in the prediction error criterionminimization. The method is also applied to experimental data.

  • 8.
    Hagenblad, Anna
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    PBL - något för alla?!2001In: Proceedings of the 2001 Process och produkt: 5:e universitetspedagogiska konferensen vid Linköpings universitet, 2001, Vol. 3, p. 92-95Conference paper (Other academic)
    Abstract [en]

    n/a

  • 9.
    Hagenblad, Anna
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    PBL - något för alla?!2002Report (Other academic)
    Abstract [en]

    Keywords: teaching, education, problem based learning, PBL

  • 10.
    Hagenblad, Anna
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Klein, Inger
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Comparison of Two Methods for Stochastic Fault Detection: the Parity Space Approach and Principal Component Analysis2003In: Proceedings of the 13th IFAC Symposium on System Identification, 2003Conference paper (Refereed)
    Abstract [en]

    This paper reviews and compares two methods for fault detection and isolation in a stochastic setting, assuming additive faults on input and output signals and stochastic unmeasurable disturbances. The first method is the parity space approach, analyzed in a stochastic setting. This leads to Kalman filter like residual generators, but with a FIR filter rather than an IIR filter as for the Kalman filter. The second method is to use principal component analysis (PCA). The advantage is that no model or structural information about the dynamic system is needed, in contrast to the parity space approach. We explain how PCA works in terms of parity space relations. The methods are illustrated on a simulation model of an F-16 aircraft, where six different faults are considered. The result is that PCA has similar fault detection and isolation capabilities as the stochastic parity space approach.

  • 11.
    Hagenblad, Anna
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Klein, Inger
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Comparison of Two Methods for Stochastic Fault Detection: the Parity Space Approach and Principal Component Analysis2004In: Proceedings of Reglermöte 2004, 2004Conference paper (Other academic)
    Abstract [en]

    This paper compares two methods for fault detection and isolation in a stochastic setting. We assume additive faults on input and output signals, and stochastic unmeasurable disturbances. The first method is the parity space approach, analyzed in a stochastic setting. The stochastic parity space approach is similar to a Kalman filter, but uses an FIR fiter, while the Kalman filter is IIR. This enables faster response to changes. The second method is to use PCA, principal component analysis. In this case no model is needed, but fault isolation will be more difficult. The methods are illustrated on a simulation model of an F-16 aircraft. The fault detection probabilities can be calculated explicitly for the parity space approach, and are verified by simulations. The simulations of the PCA method suggest that the residuals have similar fault detection and isolation capabilities as for the stochastic parity space approach.

  • 12.
    Hagenblad, Anna
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Klein, Inger
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Comparison of Two Methods for Stochastic Fault Detection: the Parity Space Approach and Principal Component Analysis2004Report (Other academic)
    Abstract [en]

    This paper compares two methods for fault detection and isolation in a stochastic setting. We assume additive faults on input and output signals, and stochastic unmeasurable disturbances. The first method is the parity space approach, analyzed in a stochastic setting. The stochastic parity space approach is similar to a Kalman filter, but uses an FIR fiter, while the Kalman filter is IIR. This enables faster response to changes. The second method is to use PCA, principal component analysis. In this case no model is needed, but fault isolation will be more difficult. The methods are illustrated on a simulation model of an F-16 aircraft. The fault detection probabilities can be calculated explicitly for the parity space approach, and are verified by simulations. The simulations of the PCA method suggest that the residuals have similar fault detection and isolation capabilities as for the stochastic parity space approach.

  • 13.
    Hagenblad, Anna
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Klein, Inger
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Teaching Control Theory Using Problem Based Learning2001Report (Other academic)
    Abstract [en]

    Problem Based Learning, PBL, has been used for several years, especially in the medical commmunity. At Linköping University, now also the program in Information Technology is taught using this method. We describe how PBL is used in a basic course in control theory, including linear algebra and Laplace transforms. The experience from the first three years of the course is promising. The students are in general more active and more motivated than students in traditional courses. The teachers spend roughly the same number of hours on the course, but the focus has shifted to more contact with the students.

  • 14.
    Hagenblad, Anna
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Maximum Likelihood Estimation of Wiener Models2000Report (Other academic)
    Abstract [en]

    A Wiener model consists of a linear dynamic system followed by a static nonlinearity. The input and output are measured, but not the intermediate signal. We discuss the Maximum Likelihood estimate for Gaussian measurement and process noise, and the special cases when one of the noise sources is zero.

  • 15.
    Hagenblad, Anna
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Maximum Likelihood Identification of Wiener Models with a Linear Regression Initialization1998Report (Other academic)
    Abstract [en]

    Many parametric identification routines suffer from the problem with local minima. This is true also for the prediction-error approach to identifying Wiener models, i.e. linear models with a static non-linearity at the output. We here suggest a linear regression initialization, that secures a consistent and efficient estimate, when used in conjunction with a Gauss-Newton minimization scheme.

  • 16.
    Hagenblad, Anna
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wills, Adrian
    University of Newcastle, Australia.
    Maximum Likelihood Identification of Wiener Models2009Report (Other academic)
    Abstract [en]

    The Wiener model is a block oriented model, having a linear dynamic system followed by a static nonlinearity. The dominating approach to estimate the components of this model has been to minimize the error between the simulated and the measured outputs. We show that this will, in general, lead to biased estimates if there are other disturbances present than measurement noise. The implications of Bussgang's theorem in this context are also discussed. For the case with general disturbances, we derive the Maximum Likelihood method and show how it can be efficiently implemented. Comparisons between this new algorithm and the traditional approach, confirm that the new method is unbiased and also has superior accuracy.

  • 17.
    Hagenblad, Anna
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wills, Adrian
    University of Newcastle, Australia.
    Maximum Likelihood Identification of Wiener Models2008In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 44, no 11, p. 2697-2705Article in journal (Refereed)
    Abstract [en]

    The Wiener model is a block oriented model, having a linear dynamic system followed by a static nonlinearity. The dominating approach to estimate the components of this model has been to minimize the error between the simulated and the measured outputs. We show that this will, in general, lead to biased estimates if there are other disturbances present than measurement noise. The implications of Bussgang's theorem in this context are also discussed. For the case with general disturbances, we derive the Maximum Likelihood method and show how it can be efficiently implemented. Comparisons between this new algorithm and the traditional approach, confirm that the new method is unbiased and also has superior accuracy.

  • 18.
    Hagenblad, Anna
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wills, Adrian
    University of Newcastle, Australia.
    Maximum Likelihood Identification of Wiener models: Journal Version2009Report (Other academic)
    Abstract [en]

    The Wiener model is a block oriented model, having a linear dynamic system followed by a static nonlinearity. The dominating approach to estimate the components of this model has been to minimize the error between the simulated and the measured outputs. We show that this will, in general, lead to biased estimates if there are other disturbances present than measurement noise. The implications of Bussgang's theorem in this context are also discussed. For the case with general disturbances, we derive the Maximum Likelihood method and show how it can be efficiently implemented. Comparisons between this new algorithm and the traditional approach, confirm that the new method is unbiased and also has superior accuracy.

  • 19.
    Klein, Inger
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hagenblad, Anna
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Teaching Control Theory Using Problem Based Learning2001In: Proceedings of the 12th Annual Conference on Innovations in Education for Electrical and Information Engineering, 2001Conference paper (Refereed)
    Abstract [en]

    Problem Based Learning, PBL, has been used for several years, especially in the medical commmunity. At Linköping University, now also the program in Information Technology is taught using this method. We describe how PBL is used in a basic course in control theory, including linear algebra and Laplace transforms. The experience from the first three years of the course is promising. The students are in general more active and more motivated than students in traditional courses. The teachers spend roughly the same number of hours on the course, but the focus has shifted to more contact with the students.

  • 20.
    Ljung, Lennart
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hagenblad, Anna
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Maximum Likelihood Estimation of Wiener Models2000In: Proceedings of the 39th IEEE Conference on Decision and Control, IEEE , 2000, p. 2417-2418 vol.3Conference paper (Refereed)
    Abstract [en]

    A Wiener model consists of a linear dynamic system followed by a static nonlinearity. The input and output are measured, but not the intermediate signal. We discuss the Maximum Likelihood estimate for Gaussian measurement and process noise, and the special cases when one of the noise sources is zero.

  • 21.
    Ljung, Lennart
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hagenblad, Anna
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Maximum Likelihood Identification of Wiener Models with a Linear Regression Initialization1998In: Proceedings of the 37th IEEE Conference on Decision and Control, 1998, p. 712-713 vol.1Conference paper (Refereed)
    Abstract [en]

    Many parametric identification routines suffer from the problem with local minima. This is true also for the prediction-error approach to identifying Wiener models, i.e. linear models with a static non-linearity at the output. We here suggest a linear regression initialization, that secures a consistent and efficient estimate, when used in conjunction with a Gauss-Newton minimization scheme.

  • 22.
    Ljung, Lennart
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hagenblad, Anna
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wills, Adrian
    University of Newcastle, Australia.
    Maximum Likelihood Identification of Wiener Models2008In: Proceedings of the 17th IFAC World Congress, 2008, p. 2714-2719Conference paper (Refereed)
    Abstract [en]

    The Wiener model is a block oriented model having a linear dynamicsystem followed by a static nonlinearity.The dominating approachto estimate the components of this model has been to minimize theerror between the simulated and the measured outputs. We show thatthis will in general lead to biased estimates if there is otherdisturbances present than measurement noise. The implications ofBussgangs theorem in this context are also discussed. For the casewith general disturbances we derive the Maximum Likelihood methodand show how it can be efficiently implemented. Comparisons betweenthis new algorithm and the traditional approach confirm that the newmethod is unbiased and also has superior accuracy.

1 - 22 of 22
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf