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Aerodynamic Identification using Neural Networks
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.
Saab Military Aircraft, Sweden.
1997 (English)Report (Other academic)
Abstract [en]

The use of neural networks and efficient identification algorithms in aerodynamic modeling could substantially reduce the time and work effort in going from wind tunnel and flight test data to model. The model is globally differentiable and can be inspected in any way desired. A number of structured and black box sigmoid type neural net models have been identified for mainly the C z aerodynamic coefficient in the region 0 ffi ff 60 ffi , where the aerodynamic coefficients behave highly nonlinear. The estimation data has been directly extracted from an existing aerodatabase for a generic fighter aircraft, that also has been used for validation. All available data has been used for estimation and the data is considered noiseless, so only the approximation properties of the different models are tested. Somewhat surprisingly, it is found that pure black box models with the same number of parameters as structured models utilizing physical insight, often perform better.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 1997. , 6 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 1937
Keyword [en]
Aerospace, Neural nets, Modeling
Keyword [sv]
Aerospace Neural Nets Modeling
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-55382ISRN: LITH-ISY-R-1937OAI: oai:DiVA.org:liu-55382DiVA: diva2:315999
Available from: 2010-04-29 Created: 2010-04-29 Last updated: 2014-09-16Bibliographically approved

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Automatic ControlThe Institute of Technology
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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