Nonlinear System Identification: From Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes

Nelles, Oliver

ISBN 10: 3030474380 / ISBN 13: 9783030474386

Published by Springer Nature, 2022

About this Item

2nd edition. 1253 pages. 9.25x6.10x1.97 inches. In Stock.

Seller Inventory # x-3030474380

Bibliographic Details

• Title: Nonlinear System Identification: From Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes
• Publisher: Springer Nature
• Publication Date: 2022
• Language: English
• ISBN 10: 3030474380
• ISBN 13: 9783030474386
• Binding: Hardcover
• Edition: 2nd Edition
• Condition: Brand New
• Weight: 2.55 kilograms

About this title

Synopsis

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.

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.