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.
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Cuprins
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.- IndexCumpărați această carte electronică și primiți încă 1 GRATUIT!
Limba Engleză ● Format PDF ● Pagini 95 ● ISBN 9781461462927 ● Mărime fișier 1.4 MB ● Editura Springer New York ● Oraș NY ● Țară US ● Publicat 2013 ● Descărcabil 24 luni ● Valută EUR ● ID 2648662 ● Protecție împotriva copiilor DRM social
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