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

Zhang, Erliang; Pintelon, Rik

doi:10.1016/j.automatica.2017.04.031

Cited by 16 publications

(10 citation statements)

References 16 publications

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“…Remark 3: To improve the performance of the F-PC-GESG algorithm, we introduce forgetting factors λ 1,i and λ 2 to the F-PC-GESG algorithm. Replace (24), (26) and (28) with Equations (42)- (44), and remain other formulas unchanged for the F-PC-GESG algorithm in (23)- (41):…”

Section: Set the Initial Values: Letmentioning

confidence: 99%

“…Applying the scalar system identification methods to multivariable systems may give poor performances, because multivariable systems have high-dimensional variables, complicated structures and many uncertain disturbances [23]. It attracts an increasing interest for researchers to explore more valid methods for multivariable systems [24,25]. When dealing with the multivariable systems with colored noises, the data filtering technique can be applied to reduce the influence of the noise and improve the estimation accuracy [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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Partially coupled gradient estimation algorithm for multivariable equation‐error autoregressive moving average systems using the data filtering technique

Liu

Ding

et al. 2019

IET Control Theory & Applications

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System identification provides many convenient and useful methods for engineering modeling. This paper targets the parameter identification problems for multivariable equation-error autoregressive moving average systems. To reduce the influence of the colored noises on the parameter estimation, the data filtering technique is adopted to filter the input and output data, and to transform the original system into a filtered system with white noises. Then we decompose the filtered system into several subsystems and develop a filtering based partially-coupled generalized extended stochastic gradient algorithm via the coupling concept. In contrast to the multivariable generalized extended stochastic gradient algorithm, the proposed algorithm can give more accurate parameter estimates. Finally, the effectiveness of the proposed algorithm is well demonstrated by simulation examples.

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Section: Set the Initial Values: Letmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Partially coupled gradient estimation algorithm for multivariable equation‐error autoregressive moving average systems using the data filtering technique

Liu

Ding

et al. 2019

IET Control Theory & Applications

View full text Add to dashboard Cite

show abstract

“…This assumption excludes all-pass systems which are commented to be not identifiable in the presence of white input-output noises [5], [27].…”

Section: Problem Formulationmentioning

confidence: 99%

“…The statistical properties (e.g., strong consistency, uncertainty and efficiency) of the generalized frequency domain estimator has been thoroughly studied for the single-input-single-output case [28] and the multivariable case [27]. Those statistical properties remain valid for the estimates θ [k] ,σ 2 when m → ∞.…”

Section: Estimator Using Single Frequency Windowmentioning

confidence: 99%

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Nonparametric identification of linear dynamic errors-in-variables systems

Zhang

Pintelon

2018

Automatica

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The present work handles the nonparametric identification of linear dynamic systems within an errors-in-variables framework, where the input is arbitrary and both the input and output disturbing noises are white with unknown variances. Using the property that the frequency response function and the system leakage term can be locally approximated very well by a low-order degree polynomial, a frequency domain estimator is developed, which gives consistent estimates for the frequency response function and the input-output noise variances. The consistency and uniqueness of the estimator are theoretically analyzed under mild conditions, and uncertainty bounds are also provided. The proposed method is finally validated on a simulated linear dynamic system.

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Auxiliary Model-Based Recursive Generalized Least Squares Algorithm for Multivariate Output-Error Autoregressive Systems Using the Data Filtering

Liu

Ding

2018

Circuits Syst Signal Process

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

Cited by 16 publications

References 16 publications

Partially coupled gradient estimation algorithm for multivariable equation‐error autoregressive moving average systems using the data filtering technique

Partially coupled gradient estimation algorithm for multivariable equation‐error autoregressive moving average systems using the data filtering technique

Nonparametric identification of linear dynamic errors-in-variables systems

Auxiliary Model-Based Recursive Generalized Least Squares Algorithm for Multivariate Output-Error Autoregressive Systems Using the Data Filtering

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