Polynomial input–output recursive models are widely used in nonlinear model identification for their flexibility and representation capabilities. Several identification algorithms are available in the literature dealing both with model selection and parameter estimation, based on various criteria. Previous works have shown the limits of the classical prediction error minimization approach, and suggested the use of a simulation error minimization approach for better model selection. The present paper 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 achieve significant performance improvements with respect to previously proposed approaches.

Simulation Error Minimization-Based Identification of Polynomial Input­-Output Recursive Models

FARINA, MARCELLO;PIRODDI, LUIGI
2009

Abstract

Polynomial input–output recursive models are widely used in nonlinear model identification for their flexibility and representation capabilities. Several identification algorithms are available in the literature dealing both with model selection and parameter estimation, based on various criteria. Previous works have shown the limits of the classical prediction error minimization approach, and suggested the use of a simulation error minimization approach for better model selection. The present paper 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 achieve significant performance improvements with respect to previously proposed approaches.
Proceedings of 15th IFAC Symposium on System Identification
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/500992
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