| 1. |
- Lindskog, Peter, 1965-, et al.
(författare)
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Ensuring Certain Physical Properties in Black Box Models by Applying Fuzzy Techniques
- 1997
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Ingår i: Proceedings of the 11th IFAC Symposium on System Identification. - Linköping : Linköping University Electronic Press. - 0080425925 ; , s. 721-727
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- We consider the situation where a nonlinear physical system is identified from input-output data. In case no specific physical structural knowledge about the system is available, parameterized grey box models cannot be used. Identification in black-box-type of model structures is then the only alternative, and general approaches like neural nets, neuro-fuzzy models, etc., have to be applied.However, certain non-structural knowledge about the system is sometimes available. It could be known, e.g., that the step response is monotonic, or that the steady-state gain curve is monotonic. The question is then how to utilize and maintain such knowledge in a black box framework.In this paper we show how to incorporate this type of prios information in an otherwise black box environment, by applying a specific fuzzy model structure, with strict parametric constraints. The usefulness of the apporach is illustrated by experiments on real-world data.
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| 2. |
- Lindskog, Peter, et al.
(författare)
-
Ensuring Monotonic Gain Characteristics in Estimated Models by Fuzzy Model Structures
- 2007
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- We consider the situation where a non-linear physical system is identified from input-output data. In case no specific physical structural knowledge about the system is available, parameterized grey-box models cannot be used. Identification in black-box type of model structures is then the only alternative, and general approaches like neural nets, neuro-fuzzy models, etc., have to be applied. However, certain non-structural knowledge about the system is sometimes available. It could be known, e.g., that the step response is monotonic, or that the steady-state gain curve is monotonic. The main question is then how to utilize and maintain such information in an otherwise black-box framework. In this paper we show how this can be done, by applying a specific fuzzy model structure, with strict parametric constraints. The usefulness of the approach is illustrated by experiments on real-world data.
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| 3. |
- Lindskog, Peter, et al.
(författare)
-
Ensuring Monotonic Gain Characteristics in Estimated Models by Fuzzy Model Structures
- 2000
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Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 36:2, s. 311-317
-
Tidskriftsartikel (refereegranskat)abstract
- We consider the situation where a non-linear physical system is identified from input-output data. In case no specific physical structural knowledge about the system is available, parameterized grey-box models cannot be used. Identification in black-box type of model structures is then the only alternative, and general approaches like neural nets, neuro-fuzzy models, etc., have to be applied. However, certain non-structural knowledge about the system is sometimes available. It could be known, e.g., that the step response is monotonic, or that the steady-state gain curve is monotonic. The main question is then how to utilize and maintain such information in an otherwise black-box framework. In this paper we show how this can be done, by applying a specific fuzzy model structure, with strict parametric constraints. The usefulness of the approach is illustrated by experiments on real-world data.
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| 4. |
- Lindskog, Peter, et al.
(författare)
-
Tools for semi-physical modelling
- 1996
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Semiphysical modelling is often interpreted as an application of system identification where physical insight into the application is used to come up with suitable non-linear transformations of the raw measurements so as to allow for a good model structure. This modelling procedure is less ‘ambitious’ than those used for traditional physical modelling in that no complete physical structure is sought, just suitable inputs and outputs that can be subjected to more or less standard model structures such as linear regressions. In this paper we discuss a semiphysical modelling procedure and various tools supporting it. These include constructive algorithms originating from commutative and differential algebra as well as more informal tools such as the programming environment.
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| 5. |
- Lindskog, Peter, et al.
(författare)
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Tools for Semiphysical Modelling
- 1995
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Ingår i: International journal of adaptive control and signal processing (Print). - : Wiley. - 0890-6327 .- 1099-1115. ; 9:6, s. 509-523
-
Tidskriftsartikel (refereegranskat)abstract
- Semiphysical modelling is often interpreted as an application of system identification where physical insight into the application is used to come up with suitable non-linear transformations of the raw measurements so as to allow for a good model structure. This modelling procedure is less ‘ambitious’ than those used for traditional physical modelling in that no complete physical structure is sought, just suitable inputs and outputs that can be subjected to more or less standard model structures such as linear regressions. In this paper we discuss a semiphysical modelling procedure and various tools supporting it. These include constructive algorithms originating from commutative and differential algebra as well as more informal tools such as the programming environment.
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| 6. |
- Ljung, Lennart, 1946-, et al.
(författare)
-
An Integrated System Identification Toolbox for Linear and Nonlinear Models
- 2006
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Ingår i: Proceedings of the 14th IFAC Symposium on System Identification. - 9783902661029 ; , s. 931-936
-
Konferensbidrag (refereegranskat)abstract
- The paper describes additions to the MATLAB system identification toolbox, that handle also the estimation of nonlinear models. Both structured grey-box models and general, flexible black-box models are covered. The idea is that the look and feel of the syntax, and the graphical user interface should be as close as possible to the linear case.
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| 7. |
- Ljung, Lennart, 1946-, et al.
(författare)
-
An Integrated System Identification Toolbox for Linear and Nonlinear Models
- 2007
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The paper describes additions to the MATLAB system identification toolbox, that handle also the estimation of nonlinear models. Both structured grey-box models and general, flexible black-box models are covered. The idea is that the look and feel of the syntax, and the graphical user interface should be as close as possible to the linear case.
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| 8. |
- Ljung, Lennart, 1946-, et al.
(författare)
-
Estimation of Grey Box and Black Box Models for Non-Linear Circuit Data
- 2007
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Identification of non-linear systems is a challenge, due to the richness of both model structures and estimation approaches. As a case study, in this paper we test a number of methods on a data set collected from an electrical circuit at the Free University of Brussels. These methods are based on black box and grey box model structures or on a mixture of them, which are all implemented in a forthcoming Matlab toolbox. The results of this case study illustrate the importance of the use of custom (user defined) regressors in a black box model. Based on physical knowledge or on insights gained through experience, such custom regressors allow to build efficient models with a relatively simple model structure.
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| 9. |
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| 10. |
- Ljung, Lennart, 1946-, et al.
(författare)
-
Tools for Semi-Physical Modeling
- 1994
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Ingår i: Proceedings of the 10th IFAC Symposium on System Identification. - 9780080422251 ; , s. 237-242
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Konferensbidrag (refereegranskat)abstract
- By semi-physical modeling we mean such an application of system identification, where physical insight into the application is used to come up with suitable nonlinear transformations of the raw measurements, so as to allow for a good model structure. Semi-physical modeling is less "ambitious" than physical modeling, in that no complete physical structure is sought, just suitable inputs and outputs that can be subjected to more or less standard model structures, such as linear regressions. In this contribution we discuss various tools that can support the process of semi-physical modeling. We will deal both with analytical tools such as differential algebra and more informal ones such as the programming environment.
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