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Minimum Variance Prediction for Linear Time-Varying Systems

Li, Zheng; Evans, R J and Wittenmark, Björn LU (1994) 10th IFAC Symposium on System Identification
Abstract
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.
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10th IFAC Symposium on System Identification
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bf93861c-ecd3-45c9-a1de-3dc509785f60 (old id 4810368)
date added to LUP
2014-11-23 11:07:37
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@misc{bf93861c-ecd3-45c9-a1de-3dc509785f60,
  abstract     = {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.},
  author       = {Li, Zheng and Evans, R J and Wittenmark, Björn},
  language     = {eng},
  title        = {Minimum Variance Prediction for Linear Time-Varying Systems},
  year         = {1994},
}