Title :
Model order, convergence rates and information content in noisy partial realizations
Author :
DeBrunner, V.E. ; Beex, A. A Louis
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci., Oklahoma Univ., Norman, OK, USA
Abstract :
It is shown that convergence rates of recursive algorithms for parameter estimation from noisy partial realizations depend on the structure of the chosen model. The model is analyzed by considering information unique to the parameterization-the sensitivity and interconnectedness of the model parameters. The convergence analysis is independent of model order. Useful information about relative convergence rates can be inferred even when the model order does not match that of the identified system
Keywords :
convergence of numerical methods; parameter estimation; convergence analysis; convergence rates; information content; model order; noisy partial realizations; parameter estimation; recursive algorithms; Computer science; Convergence; Extraterrestrial measurements; Fasteners; Fluctuations; Parameter estimation; Predictive models; Stochastic processes; Time factors; Time measurement;
Conference_Titel :
Circuits and Systems, 1991., Proceedings of the 34th Midwest Symposium on
Conference_Location :
Monterey, CA
Print_ISBN :
0-7803-0620-1
DOI :
10.1109/MWSCAS.1991.252211
