14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract

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Paper FrA2.4

Schoukens, Johan (Vrije Univ. Brussel), Van den Hof, Paul M.J. (Delft Univ. of Tech.), Pintelon, Rik (Vrije Univ. Brussel)

Reliability of Parametric Variance Estimates for Identified Transfer Functions

Scheduled for presentation during the Regular Session "Model Error Quantification and Model Validation" (FrA2), Friday, March 31, 2006, 11:30−11:50, Banquet Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 16, 2018

Keywords Model Validation

Abstract

Classical system identification methods result in an identified parametric plant and noise model. Due to modelling errors, the estimated variance of the plant model can fail completely to give a reasonable idea of the amplitude of the model errors while this is not detected at all in the validation test. A robust method is presented to validate the parametric variance estimate, and to indicate the reliable frequency bands where the estimated variance can be safely used as an indication of the remaining model errors. Outside these bands the model is unreliable (neither validated or invalidated).