Identification of Errors-in-Variables Systems with General Nonlinear Output Observations and with ARMA Observation Noises

Song, Qi; Huang, Zhiyong

doi:10.1007/s11424-020-9009-z

Cited by 11 publications

(2 citation statements)

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“…27 Song et al studied identification of EIV systems with general nonlinear output observations corrupted by ARMA observation noises. 28 Kang et al showed the approximate ML estimation property for the TLS algorithm, and investigated a graph subspace algorithm that is more general than the TLS algorithm. 29 Most of the existing EIV model identification methods are aimed at nontime-delay systems with complete measurement information.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Zhang et al developed a novel version of the extended ML estimator, which can deal with EIV systems containing arbitrary but persistent excitations and colored disturbing noises 27 . Song et al studied identification of EIV systems with general nonlinear output observations corrupted by ARMA observation noises 28 . Kang et al showed the approximate ML estimation property for the TLS algorithm, and investigated a graph subspace algorithm that is more general than the TLS algorithm 29 …”

Section: Introductionmentioning

confidence: 99%

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Identification of dual‐rate sampled errors‐in‐variables systems with time delays

Xie

Huang

Tao

et al. 2023

Optim Control Appl Methods

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For identification of dual‐rate sampled errors‐in‐variables (EIV) systems with time delays, this paper utilizes the polynomial transformation technique and the redundant rule to derive the augmented dual‐rate model, and proposes a bias compensation‐based least squares (BC‐LS) algorithm. The basic idea is to obtain a biased parameter estimate by using the LS algorithm, then compensate the noise‐induced bias by the estimated noise variances, and finally estimate the time delay according to the unbiased parameter estimate and the given threshold. Considering that proper threshold selection has great influence on the performance of the BC‐LS identification algorithm for high noise level, the generalized orthogonal matching pursuit (GOMP) algorithm is introduced to simultaneously estimate the biased parameters and the time delay, and the bias compensation‐based GOMP (BC‐GOMP) algorithm is further proposed to identify the dual‐rate sampled EIV systems with time delays. The simulation examples demonstrate the effectiveness of the proposed two algorithms, and the BC‐GOMP algorithm performs better especially when the modeling dataset is small.

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Section: Introductionmentioning

confidence: 99%