System Identification Using Regular and Quantized Observations
Subtitle: Applications of Large Deviations Principles
By: by Qi He, Le Yi Wang, G. George Yin
Contributors: Wang, Le Yi; Yin, G. George; SpringerLink (Online service)
Series: SpringerBriefs in Mathematics
Publisher: Springer New York; Imprint: Springer
Published: 2013
Place of publication: New York, NY
Language: eng
Format: [electronic resource] :
Number of pages: 95
ISBN-13: 9781461462927
Subjects: Control; Probability Theory and Stochastic Processes; Mathematics; Distribution (Probability theory); Control Systems Theory; System theory
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.
Table of contents: title: ​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; title: Remarks and Conclusion; title: References; title: Index.