System Identification Using Regular and Quantized Observations: Applications of Large Deviations Principles (SpringerBriefs in Mathematics) - Tapa blanda

He, Qi; Wang, Le Yi; Yin, George G.

 
9781461462910: System Identification Using Regular and Quantized Observations: Applications of Large Deviations Principles (SpringerBriefs in Mathematics)

Sinopsis

​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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Reseña del editor

'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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Otras ediciones populares con el mismo título

9781461462934: System Identification Using Regular and Quantized Observations: Applications of Large Deviations Principles

Edición Destacada

ISBN 10:  1461462932 ISBN 13:  9781461462934
Editorial: Springer, 2013
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