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Nonlinear System Identification

From Classical Approaches to Neural Networks and Fuzzy Models. Auflage 2001. 422 Abbildungen, Tabellen.…
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This 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. Th... weiterlesen
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Produktdetails
Titel: Nonlinear System Identification
Autor/en: Oliver Nelles

ISBN: 3540673695
EAN: 9783540673699
From Classical Approaches to Neural Networks and Fuzzy Models.
Auflage 2001.
422 Abbildungen, Tabellen.
Sprache: Englisch.
Springer-Verlag GmbH

6. November 2000 - gebunden - XVII

Beschreibung

This 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.

Inhaltsverzeichnis

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.
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