Oliver Nelles

Nonlinear System Identification (eBook, PDF)

From Classical Approaches to Neural Networks and Fuzzy Models

• Format: PDF

Produktbeschreibung

Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 786
• Erscheinungstermin: 9. März 2013
• Englisch
• ISBN-13: 9783662043233
• Artikelnr.: 53394350

Autorenporträt

Oliver Nelles was born in Frankfurt (Main), Germany, and got his Master's and Ph.D. degree in Electrical Engineering and Automatic Control at the Technical University of Darmstadt. After being a Post-Doc at the Department of Mechanical Engineering at UC Berkeley he worked for Siemens VDO Automotive in Regensburg. During his five years in Regensburg he was project and group leader in the field of transmission control. Since 2004 he assumed a position as Professor for Automatic Control - Mechatronics at the University of Siegen. Oliver Nelles' key research areas are: machine learning, system identification, nonlinear dynamic systems & control, design of experiments (DoE), fault diagnosis.

Inhaltsangabe

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.