A unified framework for EIV identification methods when the measurement noises are mutually correlated

Söderström, Torsten; Diversi, Roberto; Soverini, Umberto

doi:10.1016/j.automatica.2014.10.037

Cited by 11 publications

(2 citation statements)

References 25 publications

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“…A general framework has been originally introduced in [23], where the Generalized Instrumental Variable Estimation (GIVE) method was proposed with reference to SISO EIV systems affected by additive white noises. The method has been generalized to the case of correlated noises in [25]. The GIVE method provides a unique general framework for the whole class of bias-compensating methods, including iterative solutions, like the BELS methods [26].…”

Section: The Give Frameworkmentioning

confidence: 99%

Frequency domain identification of FIR models in the presence of additive input–output noise

Soverini

Söderström

2020

Automatica

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Section: The Give Frameworkmentioning

confidence: 99%

Frequency domain identification of FIR models in the presence of additive input–output noise

Soverini

Söderström

2020

Automatica

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“…[14][15][16][17] Therefore, it is necessary to develop new identification methods for EIV systems. An overview of EIV system identification methods can be found in Reference 18, including the instrumental variable method, 19 the bias-eliminating LS method, 20,21 the covariance matching method, 22,23 the maximum likelihood (ML) method, 24 the total least squares (TLS) method, 25 the asymptotic method, 26 and so on. 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.…”

Section: Introductionmentioning

confidence: 99%

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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Identification of multivariable dynamic errors-in-variables system with arbitrary inputs

Zhang

Pintelon

2017

Automatica

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A unified framework for EIV identification methods when the measurement noises are mutually correlated

Cited by 11 publications

References 25 publications

Frequency domain identification of FIR models in the presence of additive input–output noise

Frequency domain identification of FIR models in the presence of additive input–output noise

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

Identification of multivariable dynamic errors-in-variables system with arbitrary inputs

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