System Identification Using Regular and Quantized Observations Applications of Large Deviations Principles
Title:
System Identification Using Regular and Quantized Observations Applications of Large Deviations Principles
ISBN:
9781461462927
Personal Author:
Edition:
1st ed. 2013.
Publication Information New:
New York, NY : Springer New York : Imprint: Springer, 2013.
Physical Description:
XII, 95 p. 17 illus., 16 illus. in color. online resource.
Series:
SpringerBriefs in Mathematics,
Contents:
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.
Abstract:
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.
Added Corporate Author:
Language:
English