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- W2753496561 abstract "Standard system identification methods often provide biased estimates with closed-loop data. With the prediction error method (PEM), the bias issue is solved by using a noise model that is flexible enough to capture the noise spectrum. However, a too flexible noise model (i.e., too many parameters) can cause additional numerical problems for PEM. In this paper, we perform a theoretical analysis of the weighted null-space fitting (WNSF) method when a parametric noise model is not estimated. With this method, the system is first captured using a non-parametric ARX model, which is then reduced to a parametric model of interest. In the reduction step, a noise model does not need to be estimated if it is not of interest. In open loop, this still provides asymptotically efficient estimates of the dynamic model. In closed loop, the estimates are consistent, and their covariance is optimal for a non-parametric noise model. In this paper, we prove these results, which require additional technical details compared with the case with a full parametric model structure. In particular, we use a geometric approach for variance analysis, deriving a new result that will be instrumental to our end. Finally, we use a simulation study to illustrate the benefits of the method when the noise model cannot be parametrized by a low-order model." @default.
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- W2753496561 date "2017-08-13" @default.
- W2753496561 modified "2023-09-27" @default.
- W2753496561 title "Asymptotic Analysis of Semi-Parametric Weighted Null-Space Fitting Identification." @default.
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