System Identification Using Regular and Quantized Observations
Applications of Large Deviations Principles
1st Edition
Published on 11. February 2013
XII, 95 pages
978-1-4614-6292-7 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
New York
United States
Publishing group
Springer US
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XII, 95 p. 17 illus., 16 illus. in color.
File size
1,81 MB
ISBN-13
978-1-4614-6292-7 (9781461462927)
DOI
10.1007/978-1-4614-6292-7
Schweitzer Classification
Other editions
Additional editions
Qi He | Le Yi Wang | George G. Yin
System Identification Using Regular and Quantized Observations
Applications of Large Deviations Principles
Book
02/2013
1st Edition
Springer
€53.49
Shipment within 15-20 days
Content
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
