System identification using regular and quantized observations : applications of large deviations principles
著者
書誌事項
System identification using regular and quantized observations : applications of large deviations principles
（SpringerBriefs in mathematics）
Springer, c2013
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注記
Includes bibliographical references
内容説明・目次
内容説明
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.
目次
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
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