System Identification Using Regular and Quantized Observations

Applications of Large Deviations Principles
2013
ISBN: 978-1-4614-6291-0
Verlag: Springer

Buch, Englisch, 95 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 1766 g

Reihe: SpringerBriefs in Mathematics

This brief presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regular.  By considering both space complexity in terms of signal quantization and time complexity with respect to data window sizes, this study provides a new perspective to understand the fundamental relationship between probabilistic errors and resources, which may represent data sizes in computer usage, computational complexity in algorithms, sample sizes in statistical analysis and channel bandwidths in communications.

Zielgruppe

Research

Autoren/Hrsg.

He, Qi

Yin, George G.

Wang, Le Yi

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik

Inhaltsverzeichnis

Introduction and Overview.- System Identification: Formulation.- Large Deviations: An Introduction.- LDP under I.I.D. Noises.- LDP under Mixing Noises.- Applications to Battery Diagnosis.- Applications to Medical Signal Processing.-Applications to Electric Machines.- Remarks and Conclusion.- References.- Index