Buch, Englisch, 354 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 617 g
Theory and Practice
Buch, Englisch, 354 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 617 g
ISBN: 978-1-041-02621-1
Verlag: CRC Press
This book deals with the basics of parameter estimation and state estimation as the fundamental building blocks of mathematical modelling activity in the broader field of control theory. All the methods are validated using MATLAB®-based implementations with realistically simulated data for general dynamic systems, as well as for aircraft parameter estimation. This book includes several illustrative examples and chapter-end exercises.
Features:
- Provides comprehensive coverage of all issues related to parameter and state estimation.
- Discusses advanced topics related to Kalman filter, stability analysis, image centroid tracking and neural networks for parameter estimation.
- Explores convergence and stability results for the discussed methods.
- Reviews the estimation of parameters in linear/nonlinear models, and distributed fitting.
- Includes MATLAB®-based illustrative examples, and exercises.
This book is aimed at researchers and graduate students in systems and control, signal processing, estimation theory, engineering mathematics, and aerospace engineering.
Zielgruppe
Academic and Postgraduate
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
Weitere Infos & Material
1. Introduction 2. Least Squares and Maximum-Likelihood Methods 3. Kalman Filtering Methods 4. Filtering-cum-Data Fusion with State Delay and Missing Measurements 5. Gaussian-Sum Extended Kalman Filter with Lyapunov Stability Analysis 6. Gaussian-sum Information Filter with Lyapunov Stability Analysis 7. Image Centroid Tracking with Square-Root Kalman Filters 8. Image Centroid Tracking with Fuzzy-Logic-Augmented Filters 9. Image Centroid Tracking-cum-Fusion using New Factorization Filtering Algorithms 10. H-Infinity Fuzzification Filtering and Target Tracking (TT) 11. H-Infinity-Based Observer 12. Deterministic Nonlinear Estimators-Observers and Stability Results 13. Hybrid Global H-Infinity Filter 14. Neural Networks for Parameter Estimation with Lyapunov Stability 15. Interactive Multiple Modelling for Target Tracking with New Algorithms 16. Machine Learning for Estimation Appendix A Appendix B Appendix C




