| 51. |
- Gustafsson, Fredrik, et al.
(författare)
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Point-Mass Filter and Cramer-Rao Bound for Terrain-Aided Navigation
- 1997
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Ingår i: Proceedings of the 36th IEEE Conference on Decision and Control. - Linköping : Linköping University Electronic Press. - 0780341872 ; , s. 565-570 vol.1
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Konferensbidrag (refereegranskat)abstract
- The nonlinear estimation problem in navigation using terrain height variations is studied. The optimal Bayesian solution to the problem is derived. The implementation is grid based, calculating the probability of a set of points on an adaptively dense mesh. The Cramer-Rao bound is derived. Monte Carlo simulations over a commercial map shows that the algorithm, after convergence, reaches the Cramer-Rao lower bound.
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| 52. |
- Gustavsson, Ivar, et al.
(författare)
-
Identification of Processes in Closed Loop-Identifiability and Accuracy Aspects
- 1977
-
Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 13, s. 59-75
-
Tidskriftsartikel (refereegranskat)abstract
- It is often necessary in practice to perform identification experiments on systems operating in closed loop. There has been some confusion about the possibilities of successful identification in such cases, evidently due to the fact that certain common methods then fail. A rapidly increasing literature on the problem is briefly surveyed in this paper, and an overview of a particular approach is given. It is shown that prediction error identification methods, applied in a direct fashion will given correct estimates in a number of feedback cases. Furthermore, the accuracy is not necessarily worse in the presence of feedback; in fact optimal inputs may very well require feedback terms. Some practical applications are also described.
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| 53. |
- Hagenblad, Anna, et al.
(författare)
-
Maximum Likelihood Identification of Wiener Models
- 2009
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The Wiener model is a block oriented model, having a linear dynamic system followed by a static nonlinearity. The dominating approach to estimate the components of this model has been to minimize the error between the simulated and the measured outputs. We show that this will, in general, lead to biased estimates if there are other disturbances present than measurement noise. The implications of Bussgang's theorem in this context are also discussed. For the case with general disturbances, we derive the Maximum Likelihood method and show how it can be efficiently implemented. Comparisons between this new algorithm and the traditional approach, confirm that the new method is unbiased and also has superior accuracy.
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| 54. |
- Hjalmarsson, Håkan, et al.
(författare)
-
Assessing Model Quality from Data
- 1991
-
Ingår i: Proceedings of the 1990 IIASA Symposium on Modeling and Control of Systems with Uncertainty. - Linköping : Linköping University. - 9781461204435 ; , s. 167-187
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The problem of deriving error bounds for estimated transfer functions is addressed. By blending a priori knowledge and information obtained from measured data, we show how the uncertainty of transfer function estimates can be quantified. The emphasis is on errors due to model mismatch. The effects of unmodeled dynamics can be considered as bounded disturbances. Hence, techniques from set membership identification can be applied to this problem. The approach taken corresponds to weighted least squares estimation, and provides hard frequency domain transfer function error bounds.Real processes rarely are time-invariant. Hence, the unmodeled dynamics contains a time-varying part. It is important to quantify this model error as well. Herein, this is done in terms of confidence bounds for the “frozen” transfer function, i.e. the sequence of transfer functions obtained when freezing the time variable at succesive times. This method is based on the assumption that the true system is varying around some nominal system.
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| 55. |
- Hjalmarsson, Håkan, et al.
(författare)
-
How to Estimate Model Uncertainty in the Case of Under-Modelling
- 1990
-
Ingår i: Proceedings of the 1990 American Control Conference. - Linköping : Linköping University. ; , s. 323-324
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In System Identification, traditionally, the uncertainty estimate provided with the model is based on the assumption that the model structure used is capable of achieving a correct system description. This estimate is however not correct unless the parameter estimate is close to a "true" model parameter, that yields white noise residuals. The correct expression is known but more complex. The main difficulty, though, is that it is not easily estimated. We suggest a simple and explicit method for estimating the model uncertainty, applicable also to severe under-modelling. The method is illustrated by an example.
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| 56. |
- Hjalmarsson, Håkan, 1962-, et al.
(författare)
-
On Neural Network Model Structure in System Identification
- 1996
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Ingår i: Identification, Adaptation, Learning. The Science of Learning Models from Data. - Linköping : Linköping University Electronic Press. ; , s. 366-399
-
Rapport (övrigt vetenskapligt/konstnärligt)
|
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| 57. |
- Holmberg, Martin, et al.
(författare)
-
Bacteria classification based on feature extraction from sensor data
- 1998
-
Ingår i: Biotechnology Techniques. - : Kluwer Academic Publishers. - 0951-208X .- 1573-6784. ; 12:4, s. 319-324
-
Tidskriftsartikel (refereegranskat)abstract
- Data evaluation and classification have been made on measurements by an electronic nose on the headspace of samples of different types of bacteria growing on petri dishes. The chosen groups were: Escherichia coli, Enterococcus sp., Proteus mirabilis, Pseudomonas aeruginosa, and Staphylococcus saprophytica. An approximation of the response curve by time was made and the parameters in the curve fit were taken as important features of the data set. A classification tree was used to extract the most important features. These features were then used in an artificial neural network for classification. Using the ‘leave-one-out’ method for validating the model, a classification rate of 76% was obtained
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| 58. |
- Hu, Xiao-Li, et al.
(författare)
-
A General Convergence Result for Particle Filtering
- 2011
-
Ingår i: IEEE Transactions on Signal Processing. - Linköping : IEEE Signal Processing Society. - 1053-587X .- 1941-0476. ; 59:7, s. 3424-3429
-
Tidskriftsartikel (refereegranskat)abstract
- The particle filter has become an important tool in solving nonlinear filtering problems for dynamic systems. This correspondence extends our recent work, where we proved that the particle filter converges for unbounded functions, using L4-convergence. More specifically, the present contribution is that we prove that the particle filter converge for unbounded functions in the sense of Lp-convergence, for an arbitrary p ≥ 2.
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| 59. |
- Hu, Xiao-Li, et al.
(författare)
-
A Robust Particle Filter for State Estimation - with Convergence Results
- 2007
-
Ingår i: Proceedings of the 46th IEEE Conference on Decision and Control. - Linköping : Linköping University Electronic Press. - 9781424414987 - 9781424414970 ; , s. 312-317
-
Konferensbidrag (refereegranskat)abstract
- Particle filters are becoming increasingly important and useful for state estimation in nonlinear systems. Many filter versions have been suggested, and several results on convergence of filter properties have been reported. However, apparently a result on the convergence of the state estimate itself has been lacking. This contribution describes a general framework for particle filters for state estimation, as well as a robustified filter version. For this version a quite general convergence result is established. In particular, it is proved that the particle filter estimate convergences w.p.1 to the optimal estimate, as the number of particles tends to infinity.
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| 60. |
- Isaksson, Alf, et al.
(författare)
-
On Recursive Construction of Trees as Models of Dynamical Systems
- 1991
-
Ingår i: Proceedings of the 30th IEEE Conference on Decision and Control. - Linköping : Linköping University. - 0780304500 ; , s. 1686-1687 vol.2
-
Konferensbidrag (refereegranskat)abstract
- An issue that is of importance for control applications is discussed: how to construct the trees online, i.e. recursively, as more and more data become available. A theorem regarding recursive tree-building is stated and proved, and implementation issues are considered.
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