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Hybrid System Identification

Theory and Algorithms for Learning Switching Models

  • Fabien Lauer
  • Gérard Bloch
Book

Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 478)

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Fabien Lauer, Gérard Bloch
    Pages 1-14
  3. Fabien Lauer, Gérard Bloch
    Pages 15-58
  4. Fabien Lauer, Gérard Bloch
    Pages 59-75
  5. Fabien Lauer, Gérard Bloch
    Pages 77-101
  6. Fabien Lauer, Gérard Bloch
    Pages 103-140
  7. Fabien Lauer, Gérard Bloch
    Pages 141-167
  8. Fabien Lauer, Gérard Bloch
    Pages 169-182
  9. Fabien Lauer, Gérard Bloch
    Pages 183-203
  10. Fabien Lauer, Gérard Bloch
    Pages 205-226
  11. Fabien Lauer, Gérard Bloch
    Pages 227-231
  12. Back Matter
    Pages 233-253

About this book

Introduction

Hybrid System Identification helps readers to build mathematical models of dynamical systems switching between different operating modes, from their experimental observations. It provides an overview of the interaction between system identification, machine learning and pattern recognition fields in explaining and analysing hybrid system identification. It emphasises the optimization and computational complexity issues that lie at the core of the problems considered and sets them aside from standard system identification problems. The book presents practical methods that leverage this complexity, as well as a broad view of state-of-the-art machine learning methods.

The authors illustrate the key technical points using examples and figures to help the reader understand the material. The book includes an in-depth discussion and computational analysis of hybrid system identification problems, moving from the basic questions of the definition of hybrid systems and system identification to methods of hybrid system identification and the estimation of switched linear/affine and piecewise affine models. The authors also give an overview of the various applications of hybrid systems, discuss the connections to other fields, and describe more advanced material on recursive, state-space and nonlinear hybrid system identification.

Hybrid System Identification includes a detailed exposition of major methods, which allows researchers and practitioners to acquaint themselves rapidly with state-of-the-art tools. The book is also a sound basis for graduate and undergraduate students studying this area of control, as the presentation and form of the book provides the background and coverage necessary for a full understanding of hybrid system identification, whether the reader is initially familiar with system identification related to hybrid systems or not.

Keywords

Hybrid System Identification Hybrid System System Identification Switched System Piecewise Affine System Piecewise Smooth System Nonlinear Hybrid System Machine Learning Kernel Methods

Authors and affiliations

  • Fabien Lauer
    • 1
  • Gérard Bloch
    • 2
  1. 1.LORIAUniversité de Lorraine, CNRSNancyFrance
  2. 2.CRANUniversité de Lorraine, CNRSNancyFrance

Bibliographic information

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