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Ensuring Monotonic Gain Characteristics in Estimated Models by Fuzzy Model Structures
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2000 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 36, no 2, 311-317 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2000. Vol. 36, no 2, 311-317 p.
Keyword [en]
Nonlinear systems, Fuzzy modeling, Monotonicity, Model structures, Parameter estimation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-47698DOI: 10.1016/S0005-1098(99)00154-5OAI: oai:DiVA.org:liu-47698DiVA: diva2:268594
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-13

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Ljung, Lennart

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