| 11. |
- Ljung, Lennart, 1946-, et al.
(författare)
-
Identifiability Implies Robust Identifiability
- 1993
-
Ingår i: Proceedings of the 32nd IEEE Conference on Decision and Control. - Linköping : Linköping University. - 0780312988 ; , s. 567-569 vol.1
-
Konferensbidrag (refereegranskat)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.
|
|
| 12. |
|
|
| 13. |
- Ljung, Lennart, 1946-
(författare)
-
On the Estimation of Transfer Functions
- 1987
-
Ingår i: Proceedings of the 10th IFAC World Congress on Automatic Control. - Linköping : Linköping University. - 9780080357287 ; , s. 271-281
-
Rapport (övrigt vetenskapligt/konstnärligt)
|
|
| 14. |
- Ljung, Lennart, 1946-
(författare)
-
Recursive Identification Techniques
- 1982
-
Ingår i: Proceedings of the 1982 IEEE International Conference on Acoustics, Speech and Signal Processing. - Linköping : Linköping University. ; , s. 627-630
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Some basic results on recursive identification techniques and their properties are reviewed. The link between adaptive algorithms, recursive identification, and off-line identification is stressed. The fundamental character of the prediction and its gradient with respect to the adjustable parameters is pointed out.
|
|
| 15. |
- Nagy, Peter, et al.
(författare)
-
System Identification using Bond Graphs
- 1991
-
Ingår i: Proceedings of the First European Control Conference. - Linköping : Linköping University. - 9782866012809 ; , s. 2564-2569
-
Konferensbidrag (refereegranskat)
|
|
| 16. |
- Ohlsson, Henrik, 1981-, et al.
(författare)
-
Direct Weight Optimization Applied to Discontinuous Functions
- 2008
-
Ingår i: 47th IEEE Conference on Decision and Control, 2008. CDC 2008. - Cancun, Mexico : IEEE. - 9781424431236 ; , s. 117-122
-
Konferensbidrag (refereegranskat)abstract
- The Direct Weight Optimization (DWO) approach is a nonparametric estimation approach that has appeared in recent years within the field of nonlinear system identification. In previous work, all function classes for which DWO has been studied have included only continuous functions. However, in many applications it would be desirable also to be able to handle discontinuous functions. Inspired by the bilateral filter method from image processing, such an extension of the DWO framework is proposed for the smoothing problem. Examples show that the properties of the new approach regarding the handling of discontinuities are similar to the bilateral filter, while at the same time DWO offers a greater flexibility with respect to different function classes handled.
|
|
| 17. |
- Ottersten, Björn, et al.
(författare)
-
Asymptotic Results for Sensor Array Processing
- 1989
-
Ingår i: Proceedings of the 1989 International Conference on Acoustics, Speech and Signal Processing. - Linköping : Linköping University. ; , s. 2266-2269
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The emphasis of this study is on direction-of-arrival (DOA) estimation of narrowband emitter signals. There are two main signal models that have been used in the sensor array problem, namely, stochastic and deterministic. A discussion is presented of the models, their effect on the Cramer-Rao lower bounds (CRB), cases where the maximum likelihood (ML) estimator asymptotically achieves this bound, and how the bounds are related. It is shown that the asymptotic variance of the ML method based on the stochastic model is lower than that of the ML method based on the deterministic model. The stochastic ML variance and CRB are based on the assumption of Gaussian emitter signals, which can be an unrealistic assumption when narrowband signals are assumed. The authors show that the ML estimator based on the Gaussian signal model achieves the stochastic CRB asymptotically, regardless of the signal distribution. Numerical evaluations of the theoretical variances for different scenarios are presented to clarify the relationship between the methods and bounds discussed.
|
|
| 18. |
- Roll, Jacob, 1974-, et al.
(författare)
-
Connections Between Optimisation-Based Regressor Selection and Analysis of Variance
- 2006
-
Ingår i: Proceedings of the 45th IEEE Conference on Decision and Control. - San Diego : IEEE CSS. - 1424401712 ; , s. 4907-4914
-
Konferensbidrag (refereegranskat)abstract
- Earlier contributions have shown that Analysis of Variance (ANOVA) can be successfully used for finding good regressors for nonlinear models in a nonlinear black-box system identification context. In this paper, it is shown that the ANOVA problem can be recast as an optimisation problem. Two modified, convex versions of the ANOVA optimisation problem are then proposed, and it turns out that they are closely related to the nn-garrote and wavelet shrinkage methods, respectively. In the case of balanced data, it is also shown that the methods have a nice orthogonality property in the sense that different groups of parameters can be computed independently.
|
|
| 19. |
- Sjöberg, Jonas, et al.
(författare)
-
Nonlinear black-box modeling in system identification: A unified overview
- 1995
-
Ingår i: Automatica. - Linköping : Elsevier BV. - 0005-1098 .- 1873-2836. ; 31:12, s. 1691-1724
-
Tidskriftsartikel (refereegranskat)abstract
- A nonlinear black-box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics. There has been considerable recent interest in this area, with structures based on neural networks, radial basis networks, wavelet networks and hinging hyperplanes, as well as wavelet-transform-based methods and models based on fuzzy sets and fuzzy rules. This paper describes all these approaches in a common framework, from a user's perspective. It focuses on what are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system-identification application of these techniques. It is pointed out that the nonlinear structures can be seen as a concatenation of a mapping form observed data to a regression vector and a nonlinear mapping from the regressor space to the output space. These mappings are discussed separately. The latter mapping is usually formed as a basis function expansion. The basis functions are typically formed from one simple scalar function, which is modified in terms of scale and location. The expansion from the scalar argument to the regressor space is achieved by a radial- or a ridge-type approach. Basic techniques for estimating the parameters in the structures are criterion minimization, as well as two-step procedures, where first the relevant basis functions are determined, using data, and then a linear least-squares step to determine the coordinates of the function approximation. A particular problem is to deal with the large number of potentially necessary parameters. This is handled by making the number of 'used' parameters considerably less than the number of 'offered' parameters, by regularization, shrinking, pruning or regressor selection. Copyright © 1995 Elsevier Science Ltd All rights reserved.
|
|
| 20. |
- Stenman, Anders, et al.
(författare)
-
Just-in-Time Models for Dynamical Systems
- 1996
-
Ingår i: Proceedings of Reglermöte 1996. - Linköping : Linköping University Electronic Press. ; , s. 285--289, s. 1115-1120 vol.1
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The concept of just in time models is introduced for models that are not estimated until they are really needed. The idea is to store all observations of the process in a database, and then estimate a local model at the current working point. The variance/bias tradeoff is optimized locally by adapting the number of data and their relative weighting. This is in contrast to general nonlinear black-box models, like neural networks, where the performance is optimized globally.
|
|
