Nonlinear System Identification

Name: Nonlinear System Identification
Brand: Springer Nature Switzerland AG
SKU: 9783030474386
Price: 614.62 RON
Availability: InStock

From Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes

Autor: Oliver Nelles

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Legare: Copertă tare

Editura: Springer Nature Switzerland AG

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This book provides engineers and scientists in academia and industry with a thorough understanding o...

Informații despre carte

Autor

Oliver Nelles

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

2022

Pagini

1225

EAN

9783030474386

Enbook ID

28700625

Editura

Springer Nature Switzerland AG

Greutate

2484

Dimensiuni

242 x 163 x 51

Descriere completă

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.

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Nonlinear System Identification

From Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes

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