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Switching and Learning in Feedback Systems

European Summer School on Multi-Agent Control, Maynooth, Ireland, September 8-10, 2003, Revised Lectures and Selected Papers

  • Roderick Murray-Smith
  • Robert Shorten

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3355)

Table of contents

  1. Front Matter
  2. Switching and Control

    1. Robert Shorten, Oliver Mason, Kai Wulff
      Pages 31-46
    2. Alexandra Grancharova, Tor Arne Johansen
      Pages 47-97
  3. Gaussian Processes

    1. Joaquin Quiñonero-Candela, Carl Edward Rasmussen
      Pages 98-127
    2. Jian Qing Shi, Roderick Murray-Smith, D. Mike Titterington, Barak A. Pearlmutter
      Pages 128-139
    3. Daniel Sbarbaro, Roderick Murray-Smith
      Pages 140-157
    4. Juš Kocijan, Roderick Murray-Smith
      Pages 185-200
  4. Applications of Switching & Learning

    1. Miguel A. Vilaplana, Oliver Mason, Douglas J. Leith, William E. Leithead
      Pages 201-222
    2. Ngai Wong, Venkataramanan Balakrishnan, Tung-Sang Ng
      Pages 223-247
    3. Lin Xiao, Mikael Johansson, Haitham Hindi, Stephen Boyd, Andrea Goldsmith
      Pages 248-272
    4. Emanuele Ragnoli, William Leithead
      Pages 273-289
    5. Sam T. Roweis, Ruslan R. Salakhutdinov
      Pages 313-332
    6. John Williamson, Roderick Murray-Smith
      Pages 333-342
  5. Back Matter

About this book

Introduction

A central theme in the study of dynamic systems is the modelling and control of uncertain systems. While ‘uncertainty’ has long been a strong motivating factor behind many techniques developed in the modelling, control, statistics and mathematics communities, the past decade, in particular, has witnessed remarkable progress in this area with the emergence of a number of powerful newmethodsforbothmodellingandcontrollinguncertaindynamicsystems. The speci?c objective of this book is to describe and review some of these exciting new approaches within a single volume. Our approach was to invite some of the leading researchers in this area to contribute to this book by submitting both tutorial papers on their speci?c area of research, and to submit more focussed research papers to document some of the latest results in the area. We feel that collecting some of the main results together in this manner is particularly important as many of the important ideas that emerged in the past decade were derived in a variety of academic disciplines. By providing both tutorial and researchpaperswehopetobeabletoprovidetheinterestedreaderwithsu?cient background to appreciate some of the main concepts from a variety of related, but nevertheless distinct ?elds, and to provide a ?avor of how these results are currently being used to cope with ‘uncertainty. ’ It is our sincere hope that the availability of these results within a single volume will lead to further cro- fertilization of ideas and act as a spark for further research in this important area of applied mathematics.

Keywords

Monte Carlo Simulation Non-Para algorithmic learning algorithms calculus complexity dynamic systems learning machine learning modeling multi-agent systems networked control optimization switching theory uncertainty

Editors and affiliations

  • Roderick Murray-Smith
    • 1
  • Robert Shorten
    • 2
  1. 1.Hamilton InstituteNUI MaynoothIreland
  2. 2.Hamilton InstituteNUIMIreland

Bibliographic information

  • DOI https://doi.org/10.1007/b105497
  • Copyright Information Springer-Verlag Berlin Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-24457-8
  • Online ISBN 978-3-540-30560-6
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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