DocumentCode :
176981
Title :
Asymptotical behavior of parameter estimation in prediction error framework
Author :
Caiyun Chen ; Zhibin Yan
Author_Institution :
Natural Sci. Res. Center, Harbin Inst. of Technol., Harbin, China
fYear :
2014
fDate :
May 31 2014-June 2 2014
Firstpage :
4496
Lastpage :
4500
Abstract :
In prediction error method, it is known that the sequence of the criterion function converges uniformly in the parameter with probability one as the length of the input-output data tends to infinity. When the minimizing points of the limiting function criterion are not unique, the convergence of parameter estimation is not guaranteed in general. Two cases are distinguished. The case one is that the set of the minimizing points of the limiting function criterion is a continuum, and the case two is that the points of that set are isolated. Some interesting phenomena relating to the different cases are shown about the asymptotical behavior of parameter estimation through examples and theoretical analysis.
Keywords :
convergence; parameter estimation; probability; asymptotical behavior; convergence; criterion function; input-output data; limiting function criterion; parameter estimation; prediction error framework; prediction error method; probability; Convergence; Estimation; Limiting; Measurement; Parameter estimation; Vectors; White noise; Hausdorff metric; parameter estimation; prediction error method; system identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-3707-3
Type :
conf
DOI :
10.1109/CCDC.2014.6852974
Filename :
6852974
Link To Document :
بازگشت