Nonlinear system identification : from classical approaches to neural networks and fuzzy models
"The book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. Additionally, it provides the reader with the necessary background on optimization techniques making the book self-contained. The emphasis is put on modern methods based on neural networks and fuzzy systems without neglecting the classical approaches. The entire book is written from an engineering point-of-view, focusing on the intuitive understanding of the basic relationships. This is supported by many illustrative figures. Advanced mathematics is avoided. Thus, the book is suitable for last year undergraduate and graduate courses as well as research and development engineers in industries."--Jacket
xvii, 785 pages : illustrations ; 24 cm
9783540673699, 9783642086748, 3540673695, 3642086748
44883772
1. Introduction
pt. I. Optimization Techniques
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
pt. 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. Summary of Part II
pt. III. Dynamic Models
16. Linear Dynamic System Identification
17. Nonlinear Dynamic System Identification
18. Classical Polynomial Approaches
19. Dynamic Neural and Fuzzy Models
20. Dynamic Local Linear Neuro-Fuzzy Models
21. Neural Networks with Internal Dynamics
pt. IV. Applications
22. Applications of Static Models
23. Applications of Dynamic Models
24. Applications of Advanced Methods
A. Vectors and Matrices
link.springer.com Full text available from Springer Nature Book Archives Millennium (2000-2004)