Nonlinear System Identification: From Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes 2nd Edition Oliver Nelles 3030474399 9783030474393

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Product details:

• ISBN 10: 3030474399
• ISBN 13: 9783030474393
• Author: Oliver Nelles

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.

Table of contents:

Part I. Optimization

2. Introduction to Optimization

3. Linear Optimization

4. Nonlinear Local Optimization

5. Nonlinear Global Optimization

6. Unsupervised Learning Techniques

7. Model Complexity Optimization

8. Summary of Part I

Part II. Static Models

9. Introduction to Static Models

10. Linear, Polynomial, and Look-Up Table Models

11. Neural Networks

12. Fuzzy and Neuro-Fuzzy Models

13. Local Linear Neuro-Fuzzy Models: Fundamentals

14. Local Linear Neuro-Fuzzy Models: Advanced Aspects

15. Input Selection for Local Model Approaches

16. Gaussian Process Models (GPMs)

17. Summary of Part II

Part III. Dynamic Models

18. Linear Dynamic System Identification

19. Nonlinear Dynamic System Identification

20. Classical Polynomial Approaches

21. Dynamic Neural and Fuzzy Models

22. Dynamic Local Linear Neuro-Fuzzy Models

23. Neural Networks with Internal Dynamics

Part IV. Applications

24. Applications of Static Models

25. Applications of Dynamic Models

26. Design of Experiments

27. Input Selection Applications

28. Applications of Advanced Methods

29. LMN Toolbox

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