Ensuring Monotonic Gain Characteristics in Estimated Models by Fuzzy Model Structures
2007 (English)Report (Other academic)
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
Linköping: Linköping University Electronic Press, 2007. , 10 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2802
Nonlinear systems, Fuzzy modeling, Monotonicity, Model structures, Parameter estimation
IdentifiersURN: urn:nbn:se:liu:diva-55835ISRN: LiTH-ISY-R-2802OAI: oai:DiVA.org:liu-55835DiVA: diva2:316703