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System Identification Using Regular and Quantized Observations [electronic resource] :Applications of Large Deviations Principles / by Qi He, Le Yi Wang, G. George Yin.

by He, Qi [author.]; Wang, Le Yi [author.]; Yin, G. George [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Mathematics: Publisher: New York, NY : Springer New York : 2013.Description: XII, 95 p. 17 illus., 16 illus. in color. online resource.ISBN: 9781461462927.Subject(s): Mathematics | Systems theory | Distribution (Probability theory) | Mathematics | Systems Theory, Control | Control | Probability Theory and Stochastic ProcessesDDC classification: 519 Online resources: Click here to access online
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.
In: Springer eBooksSummary: 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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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.

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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