| 1. |
- Akçay, Hüseyin, et al.
(författare)
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On the Choice of Norms in System Identification
- 1994
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Ingår i: Proceedings of the 10th IFAC Symposium on System Identification. - Linköping : Linköping University. - 9780080422251 ; , s. 103-108
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Rapport (övrigt vetenskapligt/konstnärligt)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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| 2. |
- Hjalmarsson, Håkan, 1962-, et al.
(författare)
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A Unifying View of Disturbances in Identification
- 1994
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Ingår i: Proceedings of the 10th IFAC Symposium on System Identification. - Linköping : Linköping University. - 9780080422251 ; , s. 73-78
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Konferensbidrag (refereegranskat)
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| 3. |
- Andersson, Torbjörn, et al.
(författare)
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Identification Aspects of Inter-Sample Input Behavior
- 1994
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Ingår i: Proceedings of the 10th IFAC Symposium on System Identification. - Linköping : Linköping University. - 9780080422251 ; , s. 137-142
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Konferensbidrag (refereegranskat)abstract
- In this contribution aspects of inter-sample input signal behavior are examined. The starting point is that parametric identification always is performed on basis of discrete-time data. This is valid for identification of discrete-time models as well as continuous-time models. The usual assumptions on the input signal are; i) it is band-limited, ii) it is piecewise constant or iii) it is piecewise linear. One point made in this paper is that if a discrete-time model is used, the best possible (in the model structure) adjustment to data is made. This is independent of the assumption on the input signal. However, a transformation of the obtained discrete model to a continuous one is not possible without additional assumptions on the input signal. The other point made is that the frequency functions of the discrete models very well coincides with the frequency functions of the discretized continuous time models and the continuous time transfer function fitted in the frequency domain.
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| 4. |
- Guo, Lei, et al.
(författare)
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Performance Analysis of General Tracking Algorithms
- 1994
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Ingår i: Proceedings of the 33rd IEEE Conference on Decision and Control. - Linköping : Linköping University. - 0780319680 ; , s. 2851-2855 vol.3
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- A general family of tracking algorithms for linear regression models is studied. It includes the familiar LMS (gradient approach), RLS (recursive least squares) and KF (Kalman filter) based estimators. The exact expressions for the quality of the obtained estimates are complicated. Approximate, and easy-to-use, expressions for the covariance matrix of the parameter tracking error are developed. These are applicable over whole time interval, including the transient and the approximation error can be explicitly calculated.
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| 5. |
- Ljung, Lennart, 1946-
(författare)
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Aspects on Accelerated Convergence in Stochastic Approximation Schemes
- 1994
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Ingår i: Proceedings of the 33rd IEEE Conference on Decision and Control. - Linköping : Linköping University. - 0780319680 ; , s. 1649-1652 vol.2
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- So called accelerated convergence is an ingenuous idea to improve the asymptotic accuracy in stochastic approximation (gradient based) algorithms. The estimates obtained from the basic algorithm are subjected to a second round of averaging, which leads to optimal accuracy for estimates of time-invariant parameters. In this contribution some simple and approximate calculations are used to get some intuitive insight into these mechanisms. Of particular interest is to investigate the properties of accelerated convergence schemes in tracking situations.
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| 6. |
- Ljung, Lennart, 1946-, et al.
(författare)
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Comparison of Three Classes of Identification Methods
- 1994
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Ingår i: Proceedings of the 10th IFAC Symposium on System Identification. - Linköping : Linköping University. - 9780080422251 ; , s. 175-180
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Rapport (övrigt vetenskapligt/konstnärligt)
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| 7. |
- Ljung, Lennart, 1946-
(författare)
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From Data to Model : A Guided Tour of System Identification
- 1994
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Ingår i: Proceedings of the 1994 IEEE International Conference on Control. - Linköping : Linköping University. - 0852966105 ; , s. 422-430 vol.1
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this paper we give an overview and discussion of the basic steps of system identification. The four main ingredients of the process that takes us from observed data to a validated model are: (1) the data itself, (2) the set of candidate models, (3) the criterion of fit, and (4) the validation procedure. We discuss how these ingredients can be blended to a useful mix for model-building in practice.
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| 8. |
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| 9. |
- Ljung, Lennart, 1946-
(författare)
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System Identification in a MIC Perspective
- 1994
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Ingår i: Modeling, Identification and Control. - Linköping : Norwegian Society of Automatic Control. - 0332-7353 .- 1890-1328. ; 15:3, s. 153-159
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Tidskriftsartikel (refereegranskat)abstract
- The paper describes some sibjective aspects on some current research topics in system identification. A 'classical' standpoint is taken regarding complexity models. The need for specific tools for 'semi-physical-modeling' is also pointed out, and a discussion on different disturbance models is also included.
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| 10. |
- Sjöberg, Jonas, et al.
(författare)
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Neural Networks in System Identification
- 1994
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Ingår i: Proceedings of the 10th IFAC Symposium on System Identification. - Linköping : Linköping University. - 9780080422251 ; , s. 49-72
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Konferensbidrag (refereegranskat)abstract
- Neural Networks are non-linear black-box model structures, to be used with conventional parameter estimation methods. They have good general approximation capabilities for reasonable non-linear systems. When estimating the parameters in these structures, there is also good adaptability to concentrate on those parameters that have the most importance for the particular data set.
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