| 1. |
- Hjalmarsson, Håkan, 1962-, et al.
(författare)
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Least-squares estimation of a class of frequency functions : A finite sample variance expression
- 2006
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Ingår i: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 42:4, s. 589-600
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Tidskriftsartikel (refereegranskat)abstract
- A new expression for the variance of scalar frequency functions estimated using the least-squares method is presented. The expression is valid for finite sample size and for a class of model structures, which includes finite impulse response, Laguerre and Kautz models, when the number of estimated parameters coincides with the number of excitation frequencies of the input. The expression gives direct insight into how excitation frequencies and amplitudes affect the accuracy of frequency function estimates. With the help of this expression, a severe sensitivity of the accuracy with respect to the excitation frequencies is exposed. The relevance of the expression when more excitation frequencies are used is also discussed.
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| 2. |
- Ninness, B., et al.
(författare)
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Analysis of the variability of joint input-output estimation methods
- 2005
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Ingår i: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 41:7, s. 1123-1132
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Tidskriftsartikel (refereegranskat)abstract
- It has been recently established that, when estimating parametric models on the basis of closed loop data, the frequency domain variability of direct and various indirect methods may significantly differ from one another. This paper continues this work by analysing the performance of certain common joint input-output estimation methods.
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| 3. |
- Ninness, B., et al.
(författare)
-
On the frequency domain accuracy of closed-loop estimates
- 2005
-
Ingår i: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 41:7, s. 1109-1122
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Tidskriftsartikel (refereegranskat)abstract
- It has been argued that the frequency domain accuracy of high model-order estimates obtained on the basis of closed-loop data is largely invariant to whether direct or indirect approaches are used. The analysis underlying this conclusion has employed variance expressions that are asymptotic both in the data length and the model order, and hence are approximations when either of these are finite. However, recent work has provided variance expressions that are exact for finite (possibly low) model order, and hence can potentially deliver more accurate quantification of estimation accuracy. This paper, and a companion one, revisits the study of identification from closed-loop data in light of these new quantifications and establishes that, under certain assumptions, there can be significant differences in the accuracy of frequency response estimates. These discrepencies are established here and in the companion paper to be dependent on what type of direct, indirect or joint input-output identification strategy is pursued.
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