| 1. |
- Lind, Ingela
(författare)
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Model Order Selection of N-FIR Models by the Analysis of Variance Method
- 2000
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Ingår i: Proceedings of the 12th IFAC Symposium on System Identification. - 9780080435459 ; , s. 367-372
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Konferensbidrag (refereegranskat)abstract
- Identification of non-linear finite impulse response (N-FIR) models is studied. In particular the selection of model structure, i.e., to find the contributing input time lags, is examined. A common method, exhaustive search among models with all possible combinations of the input time lags, has some undesired drawbacks, as a tendency that the minimization algorithm gets stuck in local minima and heavy computations. To avoid these drawbacks we need to know the model structure prior to identifying a model. In this report we show that a statistical method, the multivariate analysis of variance, is a good alternative to exhaustive search in the identification of the structure of non-linear FIR-models. We can reduce the risks of getting an erroneous model structure due to the non-convexity of the minimization problems, reduce the computation time needed and also get a good estimate of how far we can enhance the fit of the desired model.
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| 2. |
- Ljung, Lennart, 1946-
(författare)
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Model Error Modeling and Control Design
- 2000
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Ingår i: Proceedings of the 12th IFAC Symposium on System Identification. - Linköping : Linköping University Electronic Press. - 9780080435459
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Konferensbidrag (refereegranskat)abstract
- Model validation and estimating the size of a possible model error is a central aspect of System Identification. In this contribution we discuss the model error concepts and model error modeling for control design. Of special interest is how to make use of periodic inputs, and how to deal with non-linear error models. The discussion is limited to SISO models and stability robustness issues.
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| 3. |
- Ljung, Lennart, 1946-
(författare)
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Teaching System Identification : Goals and Formats of Different Courses
- 2000
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Ingår i: Proceedings of the 12th IFAC Symposium on System Identification. - Linköping : Linköping University Electronic Press. - 9780080435459
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Konferensbidrag (refereegranskat)abstract
- Four cases of System Identification courses are discussed and described: A short introduction to participants without any prior knowledge, a course for industry, an undergraduate course and a graduate course.The discussion is carried out in terms of how to convey six basic messages to the course participants.
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| 4. |
- Ljung, Lennart, 1946-
(författare)
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Version 5 of the System Identification Toolbox for Use with MATLAB - WIth Object Orientation
- 2000
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Ingår i: Proceedings of the 12th IFAC Symposium on System Identification. - Oxford : Pergamon Press. - 9780080435459 ; , s. 703-708
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Konferensbidrag (refereegranskat)abstract
- Version 5 of the System Identication Toolbox is entirely rewritten, making use of MATLAB 5's objects. While the old syntax is still honored, the object orientation gives a substantial improvement of user interaction with model properties and algorithm options. In addition to the new objects, version 5 has a number of new features: handling of multiple data sets, free state-space parameterizations, estimating initial conditions for input-output models, etc.
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| 5. |
- Reinelt, Wolfgang, et al.
(författare)
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On Model Error Modeling in Set Membership Identification
- 2000
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Ingår i: Proceedings of the 2000 IFAC Symposium on System Identification. - 9780080435459 ; , s. 169-174
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Konferensbidrag (refereegranskat)abstract
- A recent perspective on model error modeling is applied to set membership identification techniques in order to highlight the separation between unmodeled dynamics and noise. Model validation issues are also easily addressed in the proposed framework. The computation of the minimum noise bound for which a nominal model is not falsified by i/o data, can be used as a rationale for selecting an appropriate model class. Uncertainty is evaluated in terms of the frequency response, so that it can be handled by H∞ control techniques.
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| 6. |
- Stenman, Anders, et al.
(författare)
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Model-on-Demand Identification for Control : An Experimental Study and Feasibility Analysis for MOD-Based Predictive Control
- 2000
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Ingår i: Proceedings of the 12th IFAC Symposium on System Identification. - Linköping : Linköping University Electronic Press. - 9780080435459 ; , s. 439-444
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Konferensbidrag (refereegranskat)abstract
- An experimental study of "Model-on-Demand" (MoD) identification is made on a pilot-scale brine-water mixing tank. MoD estimation is compared against semi-physical modeling techniques using identification data generated from a systematically designed m-level Pseudo Random Sequence (PRS) input. The estimated models are the basis for evaluating the usefulness of MoD-based Model Predictive Control (MPC). For this application, MoD-MPC is shown to provide better performance at high bandwidths compared to a linear MPC controller.
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| 7. |
- Stenman, Anders
(författare)
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On Model Structure Selection for Nonparametric Prediction Methods
- 2000
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Ingår i: Proceedings of the 12th IFAC Symposium on System Identification. - Linköping : Linköping University Electronic Press. - 9780080435459 ; , s. 361-366
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Konferensbidrag (refereegranskat)abstract
- In this paper we continue to explore identification of nonlinear systems using the previously proposed concept of model-on-demand. The idea is to estimate the process dynamics locally and on-line using process data stored in a database, and has in earlier contributions proven to be capable to produce results comparable to (or better than) other nonlinear black-box approaches. The modeling part of the method is based on local polynomial modeling ideas. This has several implications on the choice of model structure, which is discussed at length in the paper. It is concluded that the NARX structure should be considered as the default choice in the local polynomial context. Furthermore, it is shown that the predictions in some situations can be enhanced by tuning other parameters that are special for the nonparametric case. The usefulness of the method is illustrated in numerical simulations. For the chosen application it is shown that the prediction errors are in order of magnitude directly comparable to more established modeling tools such as artificial neural nets.
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| 8. |
- Tjernström, Fredrik, et al.
(författare)
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L2 Model Reduction and Variance Reduction
- 2002
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Ingår i: Automatica. - New York, USA : Elsevier. - 0005-1098 .- 1873-2836. ; 38:9, s. 1517-1530, s. 1517-1530
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Tidskriftsartikel (refereegranskat)abstract
- In this contribution we examine certain variance properties of model reduction. The focus is on L2 model reduction, but some general results are also presented. These general results can be used to analyze various other model reduction schemes. The models we study are finite impulse response (FIR) and output error (OE) models. We compare the variance of two estimated models. The first one is estimated directly from data and the other one is computed by reducing a high order model, by L2 model reduction. In the FIR case we show that it is never better to estimate the model directly from data, compared to estimating it via L2 model reduction of a high order FIR model. For OE models we show that the reduced model has the same variance as the directly estimated one if the reduced model class used contains the true system.
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| 9. |
- Tjärnström, Fredrik, et al.
(författare)
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A Nonparametric Approach to Model Error Modeling
- 2000
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Ingår i: Proceedings of the 12th IFAC Symposium on System Identification. - Linköping : Linköping University Electronic Press. - 9780080435459 ; , s. 157-162
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Konferensbidrag (refereegranskat)abstract
- To validate an estimated model and evaluate its reliability is an important part of the system identification process. Recent work on model validation has shown that the use of explicit model error models provide a better way of visualizing the possible deficiencies of the nominal model. Previous contributions have mainly focused on parametric black-box models for estimating the error model. However, this requires that a correct model order for the error model has to be selected. Here we suggest an adaptive and nonparametric frequency-domain method that estimates the frequency response of the model error by an automatic procedure. A benefit with this approach is that the tuning can be done locally, i.e., that different resolutions can be used in different frequency bands. The ideas are based on local polynomial regression and utilize a statistical criterion for selecting the optimal resolution.
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| 10. |
- Tjärnström, Fredrik
(författare)
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Computing Uncertainty Regions with Simultaneous Confidence Degree using Bootstrap
- 2000
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Ingår i: Proceedings of the 12th IFAC Symposium on System Identification. - Linköping : Linköping University Electronic Press. - 9780080435459 ; , s. 1133-1138
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Konferensbidrag (refereegranskat)abstract
- We discuss the importance of constructing confidence regions of simultaneous confidence degree for certain statistics, e.g., the frequency function. In this contribution we show how bootstrap can be used to obtain reliable confidence regions of simultaneous confidence degree, independently of how many confidence regions we calculate. The procedure is illustrated by comparison with classical methods and Monte Carlo simulations. We will also provide an evaluation of the quality of the obtained confidence regions.
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