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Exact and Approximate Modeling of Linear Systems

Exact and Approximate Modeling of Linear Systems
A Behavioral Approach

Part of Monographs on Mathematical Modeling and Computation

  • Date Published: January 2006
  • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • format: Paperback
  • isbn: 9780898716030

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  • Exact and Approximate Modeling of Linear Systems: A Behavioral Approach elegantly introduces the behavioral approach to mathematical modeling, an approach that requires models to be viewed as sets of possible outcomes rather than to be a priori bound to particular representations. The authors discuss exact and approximate fitting of data by linear, bilinear, and quadratic static models and linear dynamic models, a formulation that enables readers to select the most suitable representation for a particular purpose. This book presents exact subspace-type and approximate optimization-based identification methods, as well as representation-free problem formulations, an overview of solution approaches, and software implementation. Readers will find an exposition of a wide variety of modeling problems starting from observed data. The presented theory leads to algorithms that are implemented in C language and in MATLAB.

    • Elegantly introduces the behavioral approach to mathematical modeling
    • Presents exact subspace-type and approximate optimization-based identification methods
    • Includes algorithms that are implemented in C language and in MATLAB
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    Product details

    • Date Published: January 2006
    • format: Paperback
    • isbn: 9780898716030
    • length: 184 pages
    • dimensions: 253 x 178 x 13 mm
    • weight: 0.402kg
    • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • Table of Contents

    Preface
    1. Introduction
    2. Approximate modeling via misfit minimization
    Part I. Static Problems:
    3. Weighted total least squares
    4. Structured total least squares
    5. Bilinear errors-in-variables model
    6. Ellipsoid fitting
    Part II. Dynamic Problems:
    7. Introduction to dynamical models
    8. Exact identification
    9. Balanced model identification
    10. Errors-in-variables smoothing and filtering
    11. Approximate system identification
    12. Conclusions
    Appendices
    Notation
    Bibliography
    Index.

  • Authors

    Ivan Markovsky, Katholieke Universiteit Leuven, Belgium
    Ivan Markovsky is a Postdoctoral Researcher of Electrical Engineering at Katholieke Universiteit Leuven, Belgium. His current research work is focused on identification methods in the behavioral setting and errors-in-variables estimation problems.

    Jan C. Willems , Katholieke Universiteit Leuven, Belgium
    Jan C. Willems is a full-time Visiting Professor of Electrical Engineering at Katholieke Universiteit Leuven, Belgium, with the research group on Signals, Identification, System Theory, and Automation (SISTA). His interests lie mainly in modeling, identification, control, and issues related to the foundations of systems theory.

    Sabine Van Huffel, Katholieke Universiteit Leuven, Belgium
    Sabine Van Huffel is a Professor of Electrical Engineering at Katholieke Universiteit Leuven, Belgium. Her research interests are in signal processing, numerical linear algebra, errors-in-variables regression, system identification, pattern recognition, (non)linear modeling, software, and statistics applied to biomedicine.

    Bart De Moor, Katholieke Universiteit Leuven, Belgium
    Bart De Moor is a Professor of Electrical Engineering at Katholieke Universiteit Leuven, Belgium. His research interests are in numerical linear algebra and optimization, system theory, control and identification, quantum information theory, data mining, information retrieval, and bioinformatics.

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