NONLINEAR SYSTEM IDENTIFICATION: FROM CLASSICAL APPROACHES TO NEURAL NETWORKS AND FUZZY MODELS

The book covers the most common and importantapproaches for the identification of nonlinear staticand dynamic systems. Additionally, it provides thereader with the necessary background on optimizationtechniques making the book self-contained. The emphasisis put on modern methods based on neural networks andfuzzy systems without neglecting the classicalapproaches. The entire book is written from anengineering point-of-view, focusing on the intuitiveunderstanding of the basic relationships. This issupported by many illustrative figures. Advancedmathematics is avoided. Thus, the book is suitable forlast year undergraduate and graduate courses as well asresearch and development engineers in industries.

1. Introduction
Part 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

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. Summary of Part II

Part III. Dynamic Models
16. Linear Dynamic System Identification
17. Nonlinear Dynamic System Identification
18. Classical Polynomial Aproaches
19. Dynamic Neural and Fuzzy Models
20. Dynamic Local Linear Neuro-Fuzzy Models
21. Neural Networks with Internal Dynamics

Part IV. Applications
22. Applications of Static Models
23. Applications of Dynamic Models
24. Applications of Advanced Methods

A. Vectors and Matrices
B. Statistics
References
Index