| 1. |
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| 2. |
- Akçay, Hüseyin, et al.
(author)
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On the Choice of Norms in System Identification
- 1994
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In: Proceedings of the 10th IFAC Symposium on System Identification. - Linköping : Linköping University. - 9780080422251 ; , s. 103-108
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Reports (other academic/artistic)abstract
- In this paper we discuss smooth and sensitive norms for prediction error system identification when the disturbances are magnitude bounded. Formal conditions for sensitive norms, which give an order of magnitude faster convergence of the parameter estimate variance, are developed. However, it also is shown that the parameter estimate variance convergence rate of sensitive norms is arbitrarily bad for certain distributions. A necessary condition for a norm to be statistically robust with respect to the family F(C) of distributions with support [-C, C] for some arbitrary C>0 is that its second derivative does not vanish on the support. A direct consequence of this observation is that the quadratic norm is statistically robust among all lp-norms, p⩽2<∞ for F(C).
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| 3. |
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| 4. |
- Hjalmarsson, Håkan, 1962-, et al.
(author)
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A Unifying View of Disturbances in Identification
- 1994
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In: Proceedings of the 10th IFAC Symposium on System Identification. - Linköping : Linköping University. - 9780080422251 ; , s. 73-78
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Conference paper (peer-reviewed)
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| 5. |
- Hjalmarsson, Håkan, 1962-, et al.
(author)
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Discussion of `unknown-but-bounded' disturbances in system identification
- 1993
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In: Proceedings of the IEEE Conference on Decision and Control. - San Antonio, TX, USA : Linköping University. - 0780312988 ; , s. 535-536
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Conference paper (peer-reviewed)abstract
- In this contribution we point out that a fundamental property of a disturbance is that it is independent of the input - otherwise it is rather part of the system. Based on this characterization we show that parameter convergence can be obtained not only for stochastic but also for unknown-but-bounded disturbances if the input is at our disposal.
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| 6. |
- Hjalmarsson, Håkan, 1962-, et al.
(author)
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Estimating model variance in the case of undermodeling
- 1992
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In: IEEE Transactions on Automatic Control. - Linköping : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286 .- 1558-2523. ; 37:7, s. 1004-1008
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Journal article (peer-reviewed)abstract
- A reliable quality estimate of a given model is a prerequisite for any reasonable use of the model. The model error consists of two different contributions: the bias error and the random error. In this contribution, it is shown that the size (variance) of the random error can be reliably estimated in the case where a true system description cannot be achieved in the model structure used. This consistent error estimate can differ considerably from the conventionally used variance estimate, which could thus be misleading.
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| 7. |
- Ljung, Lennart, 1946-, et al.
(author)
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Identifiability Implies Robust Identifiability
- 1993
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In: Proceedings of the 32nd IEEE Conference on Decision and Control. - Linköping : Linköping University. - 0780312988 ; , s. 567-569 vol.1
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Conference paper (peer-reviewed)abstract
- In identification from a deterministic point of view an algorithm is said to be robustly convergent if the true system is regained when the noise level tends to zero. In this paper we introduce a concept close to this performance measure: robust global identifiability. A model structure, i.e. a smoothly parametrized set of models, is said to be robustly globally identifiable if there exist an identification algorithm such that the true parameters are regained when the noise level tends to zero. We show that global identifiability implies robust global identifiability when the model structure in consideration is a characteristic set of differential polynomials.
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| 8. |
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| 9. |
- Nagy, Peter, et al.
(author)
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System Identification using Bond Graphs
- 1991
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In: Proceedings of the First European Control Conference. - Linköping : Linköping University. - 9782866012809 ; , s. 2564-2569
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Conference paper (peer-reviewed)
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| 10. |
- Viberg, Mats, et al.
(author)
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A statistical perspective on state-space modeling using subspace methods
- 1991
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In: Proceedings of the 30th IEEE Conference on Decision and Control. - Linköping : Linköping University. - 0780304500 ; , s. 1337-1342
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Conference paper (peer-reviewed)abstract
- The authors investigate aspects of subspace-based state-space identification techniques from a statistical perspective. They concentrate their efforts on a simple approach which is based on finding the range-space of the observability matrix of a state-space representation. The system description is then found using the shift-invariance property of the observability matrix. It is shown that this results in a consistent system description for multivariable output-error models if the measurement noise is white in time and independent from output to output. The asymptotic covariance of the estimated poles of the system is also derived. In the test case studied, the subspace technique performs comparably with the statistically efficient PE (prediction error) method, whereas the IV (instrumental variable) method does notably worse. Hence, the subspace technique may be a strong candidate for determining initial values for the optimization in the efficient PE method.
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