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Table of contents

  1. Front Matter
  2. Arunabha Bagchi, Ravi Mazumdar
    Pages 1-30
  3. Sergio Bittanti, Giuseppe De Nicolao
    Pages 31-46
  4. Bokor J., L. Keviczky
    Pages 47-65
  5. Gy. Michaletzky, G. Tusnády
    Pages 79-102
  6. John P. Norton, Sándor M. Veres
    Pages 137-158
  7. Han-Fu Chen, Lei Guo, Ji-Feng Zhang
    Pages 216-241
  8. G. C. Goodwin, M. Gevers, D. Q. Mayne, V. Wertz
    Pages 300-334

About this book

Introduction

This book contains a collection of survey papers in the areas of modelling, estimation and adaptive control of stochastic systems describing recent efforts to develop a systematic and elegant theory of identification and adaptive control. It is meant to provide a fast introduction to some of the recent achievements. The book is intended for graduate students and researchers interested in statistical problems of control in general. Students in robotics and communication will also find it valuable. Readers are expected to be familiar with the fundamentals of probability theory and stochastic processes.

Keywords

Markov adaptive control communication control filtering modeling partial differential equation probability theory robot robotics stability stabilization stochastic systems stochastisches System system

Bibliographic information

  • DOI https://doi.org/10.1007/BFb0009295
  • Copyright Information Springer-Verlag 1991
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-54133-2
  • Online ISBN 978-3-540-47435-7
  • Series Print ISSN 0170-8643
  • Series Online ISSN 1610-7411
  • Buy this book on publisher's site