Buch, Englisch, Band 11, 200 Seiten, Format (B × H): 156 mm x 234 mm
Buch, Englisch, Band 11, 200 Seiten, Format (B × H): 156 mm x 234 mm
Reihe: Foundations and Trends® in Systems and Control
ISBN: 978-1-68083-330-0
Verlag: Now Publishers
Reduced order models, or model reduction, have been used in many technologically advanced areas to ensure the associated complicated mathematical models remain computable. For instance, reduced order models are used to simulate weather forecast models and in the design of very large scale integrated circuits and networked dynamical systems.
For linear systems, the model reduction problem has been addressed from several perspectives and a comprehensive theory exists. Although many results and efforts have been made, at present there is no complete theory of model reduction for nonlinear systems or, at least, not as complete as the theory developed for linear systems.
This monograph presents, in a uniform and complete fashion, moment matching techniques for nonlinear systems. This includes extensive sections on nonlinear time-delay systems; moment matching from input/output data and the limitations of the characterization of moment based on a signal generator described by differential equations. Each section is enriched with examples and is concluded with extensive bibliographical notes.
This monograph provides a comprehensive and accessible introduction into model reduction for researchers and students working on non-linear systems.
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Nichtlineare Wissenschaft
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
- Mathematik | Informatik Mathematik Mathematik Interdisziplinär Systemtheorie
Weitere Infos & Material
1. Introduction. 2. Model reduction by moment matching 3. Model reduction of time-delay systems 4. Data-driven model reduction 5. Model reduction with explicit signal generators 6. Conclusions. References.
