| 1. |
- Forssell, Urban, 1970-, et al.
(författare)
-
Combining Semi-Physical and Neural Network Modeling : An Example of Its Usefulness
- 1996
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper illustrates the power of combining semi-physical and neural network modeling in an application example. It is argued that some of the problems related to the use of neural networks, such as high dimensionality of the parameter space and problems with undesired local minima, can be alleviated by this approach.
|
|
| 2. |
- Forssell, Urban, et al.
(författare)
-
Combining Semi-Physical and Neural Network Modeling : An Example of Its Usefulness
- 1997
-
Ingår i: Proceedings of the 11th IFAC Symposium on System Identification. - Linköping : Linköping University Electronic Press. - 0080425925 ; , s. 795-798
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper illustrates the power of combining semi-physical and neural network modeling in an application example. It is argued that some of the problems related to the use of neural networks, such as high dimensionality of the parameter space and problems with undesired local minima, can be alleviated by this approach.
|
|
| 3. |
- Klein, Inger, et al.
(författare)
-
Automatic Creation of Sequential Control Schemes in Polynomial Time
- 1993
-
Ingår i: Proceedings of the 32nd Conference on Decision and Control. - Linköping : Linköping University. - 0780312988 ; , s. 211-216 vol.1
-
Konferensbidrag (refereegranskat)abstract
- Of all hard- and software developed for industrial control purposes, the majority is devoted to sequential, or binary valued, control and only a minor part to classical linear control. The sequential parts of the controller are typically invoked during startup or shut-down phases to bring the system either into its normal operating region or into some safe standby region. Despite its importance, fairly little theoretical research has been devoted to this area, and sequential control programs are still created manually without much support for a systematic approach. We propose a method to create sequential control programs automatically and online upon request, for example when a plant fault has occurred. The main idea is to spend some effort off-line on modeling the process, and from this model generate the control strategy, i.e. the plan. Here we present a planning tool implemented in a real-time expert system called G2. The planning system contains algorithms for creating plans in form of minimal GRAFCET charts that show maximal parallelism. The algorithms can handle a restricted class of problems and for this class the complexity only increases polynomially with the number of state variables.
|
|
| 4. |
- Lindskog, Peter, et al.
(författare)
-
A Comparison Between Semi-Physical and Black-Box Neural Net Modeling: A Case Study
- 1995
-
Ingår i: Proceedings of the 1995 International Conference on Engineering Applications of Neural Networks. - Linköping : Linköping University. ; , s. 235-238
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper considers identification of a solar-heated house. Using prior physical knowledge and a semi-physical modeling procedure, a set of physically motivated regressors are determined. With these as inputs a reasonable neural network model of the plant is estimated
|
|
| 5. |
- Lindskog, Peter, et al.
(författare)
-
Applications of Kautz Models in System Identification
- 1993
-
Ingår i: Proceedings of the 12th IFAC World Congress. - Linköping : Linköping University. - 9780080422121 ; , s. 309-312
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- FIR, ARX or AR model structures can be used to describe many industrial processes. Simple linear regression techniques can be applied to estimate such models from experimental data. However, for low signal to noise ratios in combination with transfer function poles and noise model zeros close to the unit circle, a large number of model parameters are needed to generate adequate models. The Kautz model structure generalizes FIR, ARX and AR models. By using a priori knowledge about the dominating time constants and damping factors of the system, the model complexity is reduced, and the linear regression structure is retained. The objective of this contribution is to study an industrial example, where Kautz models have distinct advantages. The data investigated corresponds to aircraft flight flutter, which is a state when an aircraft component starts to oscillate.
|
|
| 6. |
- Lindskog, Peter, 1965-, et al.
(författare)
-
Ensuring Certain Physical Properties in Black Box Models by Applying Fuzzy Techniques
- 1997
-
Ingår i: Proceedings of the 11th IFAC Symposium on System Identification. - Linköping : Linköping University Electronic Press. - 0080425925 ; , s. 721-727
-
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.
|
|
| 7. |
- 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.
|
|
| 8. |
- Lindskog, Peter
(författare)
-
Fuzzy Identification from a Grey Box Modeling Point of View
- 1996
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The design of mathematical models of complex real-world (and typically nonlinear) systems is essential in many fields of science and engineering. The developed models can be used, e.g., to explain the behavior of the underlying system as well as for prediction and control purposes
|
|
| 9. |
- Lindskog, Peter, et al.
(författare)
-
Tools for semi-physical modelling
- 1996
-
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.
|
|
| 10. |
- 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.
|
|
