Overview
- Self-contained, no other literature needed
- Offers a user-oriented, comprehensive overview of fundamental principles to advanced methods
- Provides explanations and terminology from an engineering perspective
- Requires only a basic grasp of algebra and statistics
- Employs one consistent notation system throughout all topics
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Table of contents (31 chapters)
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Front Matter
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Static Models
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Front Matter
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Keywords
About this book
This book provides engineers and scientists in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. It equips them to apply the models and methods discussed to real problems with confidence, while also making them aware of potential difficulties that may arise in practice.
Moreover, the book is self-contained, requiring only a basic grasp of matrix algebra, signals and systems, and statistics. Accordingly, it can also serve as an introduction to linear system identification, and provides a practical overview of the major optimization methods used in engineering. The focus is on gaining an intuitive understanding of the subject and the practical application of the techniques discussed. The book is not written in a theorem/proof style; instead, the mathematics is kept to a minimum, and the ideas covered are illustrated with numerous figures, examples, and real-world applications.
In the past, nonlinear system identification was a field characterized by a variety of ad-hoc approaches, each applicable only to a very limited class of systems. With the advent of neural networks, fuzzy models, Gaussian process models, and modern structure optimization techniques, a much broader class of systems can now be handled. Although one major aspect of nonlinear systems is that virtually every one is unique, tools have since been developed that allow each approach to be applied to a wide variety of systems.
Authors and Affiliations
About the author
Oliver Nelles was born in Frankfurt (Main), Germany, and got his Master’s and Ph.D. degree in Electrical Engineering and Automatic Control at the Technical University of Darmstadt. After being a Post-Doc at the Department of Mechanical Engineering at UC Berkeley he worked for Siemens VDO Automotive in Regensburg. During his five years in Regensburg he was project and group leader in the field of transmission control. Since 2004 he assumed a position as Professor for Automatic Control – Mechatronics at the University of Siegen. Oliver Nelles’ key research areas are: machine learning, system identification, nonlinear dynamic systems & control, design of experiments (DoE), fault diagnosis.
Bibliographic Information
Book Title: Nonlinear System Identification
Book Subtitle: From Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes
Authors: Oliver Nelles
DOI: https://doi.org/10.1007/978-3-030-47439-3
Publisher: Springer Cham
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-47438-6Published: 30 September 2022
Softcover ISBN: 978-3-030-47441-6Published: 02 October 2023
eBook ISBN: 978-3-030-47439-3Published: 09 September 2020
Edition Number: 2
Number of Pages: XXVIII, 1225
Number of Illustrations: 161 illustrations in colour
Topics: Applications of Nonlinear Dynamics and Chaos Theory, Control and Systems Theory, Control, Robotics, Mechatronics, Complexity, Calculus of Variations and Optimal Control; Optimization, Simulation and Modeling