Extended accuracy analysis of a covariance matching approach for identifying errors-in-variables systems

A novel data filtering based multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systems

“…In the simulation examples, the proposed F-MISG algorithm is more accurate than the MI-ESG algorithm for the same innovation length and the introduction of the forgetting factor can effectively improve the parameter estimation accuracy of the multi-innovation identification algorithms. The basic idea of the proposed methods can be extended to identify other linear systems [38,39] and nonlinear systems [40][41][42] with colored noise.…”

Errors-in-variables system identification using structural equation modeling

“…where A = (I − B) −1 . The matrix in (16) is known as the 'model implied covariance matrix'. The estimation problem is to determine the vector ϑ that is 'compatible' with the observations in the sense that…”

“…The topic is also treated from different points of view in the books [11], [12]. A more recent development is represented by the covariance matching (CM) approach introduced in [13], [14], [15] and [16]. This approach has been shown to be related to structural equation modeling (SEM) techniques.…”

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

“…[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.…”