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Identification of errors‐in‐variables systems: An asymptotic approach
Author(s) -
Liu Xin,
Zhu Yucai
Publication year - 2017
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2751
Subject(s) - frequency domain , errors in variables models , system identification , autoregressive model , mathematics , model selection , estimation theory , variance (accounting) , identification (biology) , delta method , control theory (sociology) , computer science , algorithm , statistics , data modeling , artificial intelligence , mathematical analysis , botany , accounting , control (management) , database , estimator , business , biology
Summary This work studies the identification of errors‐in‐variables (EIV) systems. An asymptotic method (ASYM) is developed for the EIV system. Firstly, an auto regressive with exogeneous (ARX) model estimation method is proposed, which is consistent for EIV systems. Then the asymptotic variance expression of the estimated high‐order ARX model is derived, which forms the basis of the ASYM method. In parameter estimation, the ASYM starts with a high‐order ARX model estimation followed by a frequency domain weighted model reduction. The obtained model is consistent, and its efficiency needs to be investigated. Besides parameter estimation, a criterion for model order selection is proposed, which is based on frequency domain considerations, and the frequency domain error bound is established that can be used for model validation. Simulations and comparisons with other methods are used to illustrate the performance of the method.