Abstract
This paper deals with the connection between system identification and model reduction. We will from a statistical point of view discuss how to reduce the order of high-order models obtained from an identification experiment. We will apply these results to estimate transfer functions by means of a high-order FIR model and model reduction. The model reduction techniques considered are: Frequency weighted L2-norm model reduction and model reduction via a truncated frequency weighted balanced realization.
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