TY - BOOK
T1 - System Identification Using Regular and Quantized Observations: Applications of Large Deviation Principles
AU - He, Qi
AU - Wang, Le Yi
AU - Yin, George
PY - 2013
Y1 - 2013
N2 - ""¹This 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.
AB - ""¹This 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.
UR - http://handle.uws.edu.au:8081/1959.7/528200
U2 - 10.1007/978-1-4614-6292-7
DO - 10.1007/978-1-4614-6292-7
M3 - Authored Book
SN - 9781461462910
BT - System Identification Using Regular and Quantized Observations: Applications of Large Deviation Principles
PB - Springer
CY - U.S.
ER -