System identification using regular and quantized observations applications of large deviations principles / Qi He, Le Yi Wang, G. George Yin.

Author/creator He, Qi
Other author Wang, Le Yi.
Other author Yin, George, 1954-
Format Electronic
Publication InfoNew York : Springer,
Descriptionxii, 95 p. : ill. ; 24 cm.
Supplemental ContentFull text available from Springer Books
Supplemental ContentFull text available from Springer Nature - Springer Mathematics and Statistics eBooks 2013 English International
Subjects

SeriesSpringerBriefs in Mathematics
SpringerBriefs in mathematics. ^A1256596
Contents Introduction and overview -- System identification: formulation -- Large deviations: an introduction -- LDP of system identification under independent and identically distributed observation noises -- LDP of system identification under mixing observation noises -- Applications to battery diagnosis -- Applications to medical signal processing -- Applications to electric machines -- Remarks and conclusion.
Abstract 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.
Bibliography noteIncludes bibliographical references (p. 89-94) and index.
Access restrictionAvailable only to authorized users.
Technical detailsMode of access: World Wide Web
Genre/formElectronic books.
LCCN 2012955366
ISBN9781461462910 (pbk. : acid-free paper)
ISBN1461462916 (pbk. : acid-free paper)
ISBN9781461462927 (eBook)

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