System Identification Using Regular and Quantized Observations - Applications of Large Deviations Principles (Paperback, 2013 ed.)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	SpringerBriefs in Mathematics
Release date:	February 2013
First published:	2013
Authors:	Qi He • Le Yi Wang • George G. Yin
Dimensions:	235 x 155 x 5mm (L x W x T)
Format:	Paperback
Pages:	95
Edition:	2013 ed.
ISBN-13:	978-1-4614-6291-0
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Reference & Interdisciplinary > Communication studies > Information theory > Cybernetics & systems theory Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General
LSN:	1-4614-6291-6
Barcode:	9781461462910

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