System Identification Using Regular and Quantized Observations
Applications of Large Deviations Principles
(Sprache: Englisch)
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...
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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.
Inhaltsverzeichnis zu „System Identification Using Regular and Quantized Observations “
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
Bibliographische Angaben
- Autoren: Qi He , Le Yi Wang , George G. Yin
- 2013, XII, 95 Seiten, 16 farbige Abbildungen, Maße: 15,6 x 23,6 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1461462916
- ISBN-13: 9781461462910
Sprache:
Englisch
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