Title :
Optimal length of data for identification of time varying system
Author :
Tsumura, K. ; Oishi, Y.
Author_Institution :
Dept. of Math. Eng. & Inf. Phys., Tokyo Univ., Japan
fDate :
6/21/1905 12:00:00 AM
Abstract :
In this paper, we analyze system uncertainties of time varying systems caused by the identification of the least squares method and show there exists an optimal length of data for identification. The errors between identification models and true systems at each time are investigated and we show that the radius of each corresponding uncertainty can be calculated in a statistical sense. We explain that these errors are composed of noise error term and a time varying term of systems. And it is shown that there exists a tradeoff between the sizes of these terms for the length of data in identification, and the optimal length of data which minimize the radius can be calculated
Keywords :
identification; least squares approximations; minimisation; statistical analysis; time-varying systems; uncertain systems; corresponding uncertainty; least-squares method; noise error term; optimal data length; radius minimization; statistical calculation; system uncertainties; time varying system identification; Data engineering; Error analysis; Error correction; Information analysis; Least squares methods; Nonlinear systems; Physics; Robust control; Time varying systems; Uncertainty;
Conference_Titel :
Decision and Control, 1999. Proceedings of the 38th IEEE Conference on
Conference_Location :
Phoenix, AZ
Print_ISBN :
0-7803-5250-5
DOI :
10.1109/CDC.1999.827766
