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Hybrid System Identification : Theory and Algorithms for Learning Switching Models

Author: Fabien Lauer; Gérard Bloch
Publisher: Cham : Springer International Publishing : Imprint : Springer, 2019.
Series: Lecture notes in control and information sciences, 478.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Details

Genre/Form: Electronic books
Additional Physical Format: Printed edition:
Printed edition:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Fabien Lauer; Gérard Bloch
ISBN: 9783030001933 3030001938
OCLC Number: 1086547703
Description: 1 online resource (XXI, 253 pages 35 illustrations, 34 illustrations in color.) : online resource
Contents: Introduction --
System Identification --
Classification --
Hybrid System Identification --
Exact Methods for Hybrid System Identification --
Estimation of Switched Linear/Affine Models --
Estimation of Piecewise Affine Models --
Recursive and State-space Identification of Hybrid Systems --
Nonlinear Hybrid System Identification.
Series Title: Lecture notes in control and information sciences, 478.
Responsibility: by Fabien Lauer, Gérard Bloch.

Abstract:

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.

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