System Identification Using Regular and Quantized Observations

Applications of Large Deviations Principles

Nonfiction, Science & Nature, Science, Other Sciences, System Theory, Technology, Automation, Reference & Language, Reference
Cover of the book System Identification Using Regular and Quantized Observations by Qi He, Le Yi Wang, George G. Yin, Springer New York
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Author: Qi He, Le Yi Wang, George G. Yin ISBN: 9781461462927
Publisher: Springer New York Publication: February 11, 2013
Imprint: Springer Language: English
Author: Qi He, Le Yi Wang, George G. Yin
ISBN: 9781461462927
Publisher: Springer New York
Publication: February 11, 2013
Imprint: Springer
Language: English

​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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​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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