Model Equivalence-Based Identification Algorithm for Equation-Error Systems with Colored Noise

Abstract

For equation-error autoregressive (EEAR) systems, this paper proposes an identification algorithm by means of the model equivalence transformation. The basic idea is to eliminate the autoregressive term in the model using the model transformation, to estimate the parameters of the converted system and further to compute the parameter estimates of the original system using the comparative coefficient way and the model equivalence principle. For comparison, the recursive generalized least squares algorithm is given simply. The simulation results verify that the proposed algorithm is effective and can produce more accurate parameter estimates.

DOI: 10.3390/a8020280

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Cite this paper

@article{Meng2015ModelEI, title={Model Equivalence-Based Identification Algorithm for Equation-Error Systems with Colored Noise}, author={Dandan Meng and Feng Ding}, journal={Algorithms}, year={2015}, volume={8}, pages={280-291} }