Polynomial input/output (I/O) recursive models are widely used in nonlinear model identification for their flexibility and representation capabilities. Several identification algorithms are available in the literature, which deal with both model selection and parameter estimation. Previous works have shown the limitations of the classical prediction error minimisation approach in this context, especially (but not only) when the disturbance contribution is unknown, and suggested the use of a simulation error minimisation (SEM) approach for a better selection of the I/O model. This article goes a step further by integrating the model selection procedure with a simulation-oriented parameter estimation algorithm. Notwithstanding the algorithmic and computational complexity of the proposed method, it is shown that it can sometimes achieve great performance improvements with respect to previously proposed approaches. Two different parameter estimation algorithms are suggested, namely a direct SEM optimisation algorithm, and an approximate method based on multi-step prediction iteration, which displays several convenient properties from the computational point of view. Several simulation examples are shown to demonstrate the effectiveness of the suggested SEM approaches.

Identification of Polynomial Input/Output Recursive Models with Simulation Error Minimisation Methods

FARINA, MARCELLO;PIRODDI, LUIGI
2012-01-01

Abstract

Polynomial input/output (I/O) recursive models are widely used in nonlinear model identification for their flexibility and representation capabilities. Several identification algorithms are available in the literature, which deal with both model selection and parameter estimation. Previous works have shown the limitations of the classical prediction error minimisation approach in this context, especially (but not only) when the disturbance contribution is unknown, and suggested the use of a simulation error minimisation (SEM) approach for a better selection of the I/O model. This article goes a step further by integrating the model selection procedure with a simulation-oriented parameter estimation algorithm. Notwithstanding the algorithmic and computational complexity of the proposed method, it is shown that it can sometimes achieve great performance improvements with respect to previously proposed approaches. Two different parameter estimation algorithms are suggested, namely a direct SEM optimisation algorithm, and an approximate method based on multi-step prediction iteration, which displays several convenient properties from the computational point of view. Several simulation examples are shown to demonstrate the effectiveness of the suggested SEM approaches.
2012
AUT
File in questo prodotto:
File Dimensione Formato  
2012 - IJSS - FarinaPiroddi.pdf

Accesso riservato

: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 321.34 kB
Formato Adobe PDF
321.34 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/577315
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 16
  • ???jsp.display-item.citation.isi??? 11
social impact