DocumentCode :
1265209
Title :
A note on sampling and parameter estimation in linear stochastic systems
Author :
Duncan, T.E. ; Mandl, P. ; Pasik-Duncan, B.
Author_Institution :
Dept. of Math., Kansas Univ., Lawrence, KS, USA
Volume :
44
Issue :
11
fYear :
1999
fDate :
11/1/1999 12:00:00 AM
Firstpage :
2120
Lastpage :
2125
Abstract :
Numerical differentiation formulas that yield consistent least squares parameter estimates from sampled observations of linear, time invariant higher order systems have been introduced previously by Duncan et al. (1994). The formulas given by Duncan et al. have the same limiting system of equations as in the continuous time case. The formula presented in this note can be characterized as preserving asymptotically a partial integration rule. It leads to limiting equations for the parameter estimates that are different from the continuous case, but they again imply consistency. The numerical differentiation formulas given here can be used for an arbitrary linear system, which is not the case in the previous paper by Duncan et al
Keywords :
differentiation; least squares approximations; linear systems; parameter estimation; sampling methods; stochastic systems; consistency; least squares parameter estimates; linear stochastic systems; numerical differentiation formulas; parameter estimation; partial integration rule; sampling; Differential equations; Least squares approximation; Linear systems; Mathematics; Parameter estimation; Random variables; Sampling methods; Stochastic processes; Stochastic systems; Yield estimation;
fLanguage :
English
Journal_Title :
Automatic Control, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9286
Type :
jour
DOI :
10.1109/9.802928
Filename :
802928
Link To Document :
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