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Algorithms and Tools for System Identification Using Prior Knowledge
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
1994 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

One of the hardest problem in system identification is that of model structure selection. In this thesis two different kinds of a priori process knowledge are used to address this fundamental problem.

Concentrating on linear model structures, the first prior taken advantage of is knowledge about the systems' dominating time constants and resonance frequencies. The idea is to generalize FIR modeling by replacing the usual delay operator with discrete so-called Laguerre or K autz filters. The generalization is such that stability, the linear regression structure and the approximation ability of the FIR model structure is retained, whereas the prior is used to reduce the number of parameters needed to arrive at a reasonable model. Tailorized and efficient system identification algorithms for these model structures are detailed in this work. The usefulness of the proposed methods is demonstrated through concrete simulation and application studies.

The other approach is referred to as semi-physical modeling. The main idea is to use simple physical insight into the application, often in terms of a set of unstructured equations, in order to come up with suitable nonlinear transformation 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 combinations of inputs and outputs that can be subjected to more or less standard model structures, such as linear regressions. The suggested modeling procedure shows a first step where symbolic computations are employed to determine a suitable model structure - a set of regressors. We show how constructive methods from commutative and differential algebra can be applied for this. Subsequently, different numerical schemes for finding a subset of "good" regressors and for estimating the corresponding linear-in-the-parameters model are discussed. We also deal with more informal tools such as the programming environment.

Finally and perhaps more importantly, software tools supporting the suggested approaches have been designed and implemented.

Place, publisher, year, edition, pages
Linköping: Linköping University , 1994. , 131 p.
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 456
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-98092Local ID: LiU-TEK-LIC-1994:23ISBN: 91-7871-422-2OAI: diva2:652098
Available from: 2013-10-09 Created: 2013-09-29 Last updated: 2013-11-07Bibliographically approved

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