Nonlinear System Identification

Name: Nonlinear System Identification
Brand: Springer, Berlin
SKU: 9783030474416
Price: 327.53 PLN
Availability: InStock

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

Autor: Oliver Nelles

Język:

Oprawa: Miękka

Wydawca: Springer, Berlin

Dostępność: 50 % szansa

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327.53 zł

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

Informacje o książce

Autor

Oliver Nelles

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2023

strony

1225

EAN

9783030474416

Enbook ID

41555088

Wydawca

Springer, Berlin

Wymiary

155 x 235

Pełny opis

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

Informacje o książce

Pełny opis

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