Nonlinear System Identification From Classical Approaches to Neural Networks and Fuzzy Models
The goal of this book is to provide engineers and scientIsts in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. The reader will be able to apply the discussed models and methods to real problems with the necessary confidence and th...
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| Format: | eBook |
| Language: | English |
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Berlin, Heidelberg
Springer Berlin Heidelberg
2001, 2001
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| Edition: | 1st ed. 2001 |
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| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 1. Introduction
- 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
- 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
- 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
- IV. Applications
- 22. Applications of Static Models
- 23. Applications of Dynamic Models
- 24. Applications of Advanced Methods
- A. Vectors and Matrices
- A.1 Vector and Matrix Derivatives
- A.2 Gradient, Hessian, and Jacobian
- B. Statistics
- B.1 Deterministic and Random Variables
- B.2 Probability Density Function (pdf)
- B.3 Stochastic Processes and Ergodicity
- B.4 Expectation
- B.5 Variance
- B.6 Correlation and Covariance
- B.7 Properties of Estimators
- References