Modelling and Identification with Rational Orthogonal Basis Functions

  • Peter S.C. Heuberger
  • Paul M.J. Van den Hof
  • Bo Wahlberg

Table of contents

  1. Front Matter
    Pages i-xxvi
  2. Bo Wahlberg, Brett Ninness, Paul Van den Hof
    Pages 1-13
  3. Bo Wahlberg, Tomás e Oliveira Silva
    Pages 15-39
  4. Bo Wahlberg
    Pages 41-60
  5. Paul Van den Hof, Brett Ninness
    Pages 61-102
  6. Brett Ninness, Håkan Hjalmarsson
    Pages 103-159
  7. Brett Ninness, Håkan Hjalmarsson
    Pages 161-188
  8. Paul Van den Hof
    Pages 189-212
  9. József Bokor, Zoltan Szabó
    Pages 213-233
  10. József Bokor, Zoltan Szabó
    Pages 235-268
  11. Peter Heuberger
    Pages 269-296
  12. Tomás e Oliveira Silva
    Pages 297-336
  13. Peter Heuberger, Thomas de Hoog
    Pages 337-358
  14. Peter Heuberger, Thomas de Hoog
    Pages 359-373
  15. Back Matter
    Pages 375-397

About this book


Models of dynamical systems are of great importance in almost all fields of science and engineering and specifically in control, signal processing and information science. A model is always only an approximation of a real phenomenon so that having an approximation theory which allows for the analysis of model quality is a substantial concern. The use of rational orthogonal basis functions to represent dynamical systems and stochastic signals can provide such a theory and underpin advanced analysis and efficient modelling. It also has the potential to extend beyond these areas to deal with many problems in circuit theory, telecommunications, systems, control theory and signal processing.

Nine international experts have contributed to this work to produce thirteen chapters that can be read independently or as a comprehensive whole with a logical line of reasoning:


• Construction and analysis of generalized orthogonal basis function model structure;

• System Identification in a time domain setting and related issues of variance, numerics, and uncertainty bounding;

• System identification in the frequency domain;

• Design issues and optimal basis selection;

• Transformation and realization theory.


Modelling and Identification with Rational Orthogonal Basis Functions affords a self-contained description of the development of the field over the last 15 years, furnishing researchers and practising engineers working with dynamical systems and stochastic processes with a standard reference work.


communication control control theory design development identification information model modeling quality science signal processing system system identification uncertainty

Editors and affiliations

  • Peter S.C. Heuberger
    • 1
  • Paul M.J. Van den Hof
    • 1
  • Bo Wahlberg
    • 2
  1. 1.Delft Center for Systems and ControlDelft University of TechnologyDelftThe Netherlands
  2. 2.S3 - Automatic Control GroupRoyal Institute of TechnologyStockholmSweden

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag London Limited 2005
  • Publisher Name Springer, London
  • eBook Packages Engineering
  • Print ISBN 978-1-85233-956-2
  • Online ISBN 978-1-84628-178-5
  • Buy this book on publisher's site
Industry Sectors
Materials & Steel
Chemical Manufacturing
IT & Software
Oil, Gas & Geosciences