The thesis analyzes some techniques adopted for model order selection in system identification: both classical methods (cross-validation, information criteria, the F-test and the statistical tests on the residuals) and innovative ones are evaluated, such as PUMS criterion and kernel-based estimation. The theoretical description of these methods is accompanied by an experimental analysis. Two combinations of the methods are also introduced, proving that they allow a more robust order selection

Model Order Selection in System Identification: New and Old Techniques

Prando, Giulia
2013/2014

Abstract

The thesis analyzes some techniques adopted for model order selection in system identification: both classical methods (cross-validation, information criteria, the F-test and the statistical tests on the residuals) and innovative ones are evaluated, such as PUMS criterion and kernel-based estimation. The theoretical description of these methods is accompanied by an experimental analysis. Two combinations of the methods are also introduced, proving that they allow a more robust order selection
2013-10-08
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/17449