| 1. |
- Bottegal, G., et al.
(författare)
-
On maximum likelihood identification of errors-in-variables models
- 2017
-
Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 50:1, s. 2824-2829
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, we revisit maximum likelihood methods for identification of errors-in-variables systems. We assume that the system admits a parametric description, and that the input is a stochastic ARMA process. The cost function associated with the maximum likelihood criterion is minimized by introducing a new iterative solution scheme based on the expectation-maximization method, which proves fast and easily implementable. Numerical simulations show the effectiveness of the proposed method.
|
|
| 2. |
- Hjalmarsson, Håkan, 1962-, et al.
(författare)
-
Least-squares estimation of a class of frequency functions : A finite sample variance expression
- 2006
-
Ingår i: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 42:4, s. 589-600
-
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.
|
|
| 3. |
- Ninness, B., et al.
(författare)
-
Analysis of the variability of joint input-output estimation methods
- 2005
-
Ingår i: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 41:7, s. 1123-1132
-
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.
|
|
| 4. |
- Ninness, B., et al.
(författare)
-
Model structure and numerical properties of normal equations
- 2001
-
Ingår i: IEEE Transactions on Circuits And Systems Part I. - : Institute of Electrical and Electronics Engineers (IEEE). - 1057-7122 .- 1558-1268. ; 48:4, s. 425-437
-
Tidskriftsartikel (refereegranskat)abstract
- There has been recent interest in using ortho-normalized forms of fixed denominator model structures for system identification, A key motivating factor in the employment of these forms is that of improved numerical properties. Namely, for white input, perfect conditioning of the least-squares normal equations is achieved by design. However, for the more usual case of colored input spectrum, it is not clear what the numerical conditioning properties should be in relation to simpler and perhaps more natural model structures. This paper provides theoretical and empirical evidence to argue that in fact, even though the orthonormal structures are only designed to provide perfect numerical conditioning for white input, they still provide improved conditioning for a wide variety of colored inputs.
|
|
| 5. |
- Ninness, B., et al.
(författare)
-
On the Frequency Domain Accuracy of Closed Loop Estimates
- 2003
-
Ingår i: Proceedings of the IEEE Conference on Decision and Control. - 0780379241 ; , s. 5997-6002
-
Konferensbidrag (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. This paper revisits this study in light of new variance quantification results that apply for low model order and establishes that, under certain assumptions, there can be significant differences in the accuracy of frequency response estimates that are dependent on what type of direct, indirect or joint input-output identification strategy is pursued.
|
|
| 6. |
- 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
-
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.
|
|
| 7. |
- Ninness, B., et al.
(författare)
-
The effect of regularization on variance error
- 2004
-
Ingår i: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286 .- 1558-2523. ; 49:7, s. 1142-1147
-
Tidskriftsartikel (refereegranskat)abstract
- This note addresses the problem of quantifying the effect of noise induced error(so called variance error) in system estimates found via a regularised cost criterion. It builds on recent work by the authors in which expressions for nonregularised criterions are derived which are exact for finite model order. Those new expressions were established to be very different to previous quantifications that are widely used but based on asymptotic in model order arguments. A key purpose of this note is to expose a rapprochement between these new finite model order, and the preexisting asymptotic model order quantifications. In so doing, a further new result is established. Namely, that variance error in the frequency domain is dependent on the choice of the point about which regularization is affected.
|
|
| 8. |
- Ninness, B., et al.
(författare)
-
Variance Error Quantifications that are Exact for Finite Model Order
- 2003
-
Ingår i: Proceedings of the IEEE Conference on Decision and Control. - 0780379241 ; , s. 6003-6008
-
Konferensbidrag (refereegranskat)abstract
- This paper is concerned with the frequency domain quantification of noise induced errors in dynamic system estimates. Preceding seminal work on this problem provides general expressions that are approximations whose accuracy increases with observed data length and model order. In the interests of improved accuracy, this paper provides new expressions whose accuracy depends only on data length. They are therefore 'exact' for arbitrarily small true model order and apply to the general cases of Output-Error and Box-Jenkins model structures.
|
|
