Bounding Approaches to System Identification

  • Mario Milanese
  • John Norton
  • Hélène Piet-Lahanier
  • Éric Walter

Table of contents

  1. Front Matter
    Pages i-xix
  2. J. P. Norton
    Pages 1-4
  3. G. Favier, L. V. R. Arruda
    Pages 43-68
  4. K. Forsman, L. Ljung
    Pages 69-82
  5. L. Pronzato, É. Walter
    Pages 119-138
  6. S. M. Markov, E. D. Popova
    Pages 139-157
  7. M. Milanese
    Pages 169-181
  8. É. Walter, H. Piet-Lahanier
    Pages 183-197
  9. T. F. Filippova, A. B. Kurzhanski, K. Sugimoto, I. Vályi
    Pages 213-238
  10. H. Piet-Lahanier, É. Walter
    Pages 261-273

About this book


In response to the growing interest in bounding error approaches, the editors of this volume offer the first collection of papers to describe advances in techniques and applications of bounding of the parameters, or state variables, of uncertain dynamical systems. Contributors explore the application of the bounding approach as an alternative to the probabilistic analysis of such systems, relating its importance to robust control-system design.


Analysis Markov Regression algorithm complexity linear regression robot system system identification

Editors and affiliations

  • Mario Milanese
    • 1
  • John Norton
    • 2
  • Hélène Piet-Lahanier
    • 3
  • Éric Walter
    • 4
  1. 1.Dipartimento di Automatica e InformaticaPolitecnico di TorinoTorinoItaly
  2. 2.School of Electronic and Electrical EngineeringUniversity of BirminghamEdgbaston, BirminghamUK
  3. 3.Direction des Études de SynthèseSM Office National d’Études et de Recherches AérospatialesChâtillon CedexFrance
  4. 4.Laboratoire des Signaux et SystèmesCNRS-École Supérieure d’ÉlectricitéGif-sur-Yvette CedexFrance

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag US 1996
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4757-9547-9
  • Online ISBN 978-1-4757-9545-5
  • About this book