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

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...

Full description

Bibliographic Details
Main Authors: He, Qi. (Author), Wang, Le Yi. (Author), Yin, G. George. (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic
Language:English
Published: New York, NY : Springer New York : Imprint: Springer, 2013.
Series:SpringerBriefs in Mathematics,
Subjects:
Online Access:https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-4614-6292-7
LEADER 02710nam a22005175i 4500
001 13546
003 DE-He213
005 20130727071740.0
007 cr nn 008mamaa
008 130220s2013 xxu| s |||| 0|eng d
020 # # |a 9781461462927  |9 978-1-4614-6292-7 
024 7 # |a 10.1007/978-1-4614-6292-7  |2 doi 
050 # 4 |a Q295 
050 # 4 |a QA402.3-402.37 
072 # 7 |a GPFC  |2 bicssc 
072 # 7 |a SCI064000  |2 bisacsh 
072 # 7 |a TEC004000  |2 bisacsh 
082 0 4 |a 519  |2 23 
100 1 # |a He, Qi.  |e author. 
245 1 0 |a System Identification Using Regular and Quantized Observations  |b Applications of Large Deviations Principles /  |c by Qi He, Le Yi Wang, G. George Yin.  |h [electronic resource] : 
264 # 1 |a New York, NY :  |b Springer New York :  |b Imprint: Springer,  |c 2013. 
300 # # |a XII, 95 p. 17 illus., 16 illus. in color.  |b online resource. 
336 # # |a text  |b txt  |2 rdacontent 
337 # # |a computer  |b c  |2 rdamedia 
338 # # |a online resource  |b cr  |2 rdacarrier 
347 # # |a text file  |b PDF  |2 rda 
490 1 # |a SpringerBriefs in Mathematics,  |x 2191-8198 
505 0 # |a 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. 
520 # # |a 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. 
650 # 0 |a Mathematics. 
650 # 0 |a Systems theory. 
650 # 0 |a Distribution (Probability theory). 
650 1 4 |a Mathematics. 
650 2 4 |a Systems Theory, Control. 
650 2 4 |a Control. 
650 2 4 |a Probability Theory and Stochastic Processes. 
700 1 # |a Wang, Le Yi.  |e author. 
700 1 # |a Yin, G. George.  |e author. 
710 2 # |a SpringerLink (Online service) 
773 0 # |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9781461462910 
830 # 0 |a SpringerBriefs in Mathematics,  |x 2191-8198 
856 4 0 |u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-4614-6292-7 
912 # # |a ZDB-2-SMA 
950 # # |a Mathematics and Statistics (Springer-11649)