Minimum Variance Prediction for Linear Time-Varying Systems
Författare
Summary, in English
In this paper we study the problem of minimum variance prediction for linear time-varying systems. We consider the standard time-varying autoregression moving average (ARMA) model and develop a predictor which guarantees minimum variance prediction for a large class of linear time-varying systems. The predictor is developed based on a pseudocommutation technique for dealing with noncommutativity of linear time-varying operators in a transfer operator framework. We also show connections between this input-output predictor and the Kalman predictor via an example.
Avdelning/ar
Publiceringsår
1994
Språk
Engelska
Publikation/Tidskrift/Serie
IFAC Proceedings Volumes
Volym
27:8
Dokumenttyp
Konferensbidrag
Ämne
- Control Engineering
Conference name
10th IFAC Symposium on System Identification, SYSID'94
Conference date
1994-07-04
Conference place
Copenhagen, Denmark
Status
Published