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Minimum Variance Prediction for Linear Time-Varying Systems

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Author

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.

Publishing year

1994

Language

English

Publication/Series

IFAC Proceedings Volumes

Volume

27:8

Document type

Conference paper

Topic

  • Control Engineering

Conference name

10th IFAC Symposium on System Identification, SYSID'94

Conference date

1994-07-04

Conference place

Copenhagen, Denmark

Status

Published