Buch, Englisch, 317 Seiten, Format (B × H): 167 mm x 241 mm, Gewicht: 1430 g
Buch, Englisch, 317 Seiten, Format (B × H): 167 mm x 241 mm, Gewicht: 1430 g
Reihe: Systems & Control: Foundations & Applications
ISBN: 978-0-8176-4955-5
Verlag: Birkhauser Boston
This book presents recently developed methodologies that utilize quantized information in system identification and explores their potential in extending control capabilities for systems with limited sensor information or networked systems. The results of these methodologies can be applied to signal processing and control design of communication and computer networks, sensor networks, mobile agents, coordinated data fusion, remote sensing, telemedicine, and other fields in which noise-corrupted quantized data need to be processed.
is an excellent resource for graduate students, systems theorists, control engineers, applied mathematicians, as well as practitioners who use identification algorithms in their work.
Zielgruppe
Graduate
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Sensorik
- Mathematik | Informatik Mathematik Mathematik Interdisziplinär Systemtheorie
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
Weitere Infos & Material
Overview.- System Settings.- Stochastic Methods for Linear Systems.- Empirical-Measure-Based Identification: Binary-Valued Observations.- Estimation Error Bounds: Including Unmodeled Dynamics.- Rational Systems.- Quantized Identification and Asymptotic Efficiency.- Input Design for Identification in Connected Systems.- Identification of Sensor Thresholds and Noise Distribution Functions.- Deterministic Methods for Linear Systems.- Worst-Case Identification under Binary-Valued Observations.- Worst-Case Identification Using Quantized Observations.- Identification of Nonlinear and Switching Systems.- Identification of Wiener Systems with Binary-Valued Observations.- Identification of Hammerstein Systems with Quantized Observations.- Systems with Markovian Parameters.- Complexity Analysis.- Space and Time Complexities, Threshold Selection, Adaptation.- Impact of Communication Channels on System Identification.
