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Probabilistic and Other Neural Nets in Multi-Hole Probe Calibration and Flow Angularity Pattern RecognitionThe use of probabilistic (PNN) and multilayer feed forward (MLFNN) neural networks are investigated for calibration of multi-hole pressure probes and the prediction of associated flow angularity patterns in test flow fields. Both types of networks are studied in detail for their calibration and prediction characteristics. The current formalism can be applied to any multi-hole probe, however the test results for the most commonly used five-hole Cone and Prism probe types alone are reported in this article.
Document ID
19990111657
Acquisition Source
Marshall Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
Baskaran, Subbiah
(Raytheon STX United States)
Ramachandran, Narayanan
(Universities Space Research Association United States)
Noever, David
(NASA Marshall Space Flight Center Huntsville, AL United States)
Date Acquired
August 19, 2013
Publication Date
January 1, 1998
Subject Category
Cybernetics
Meeting Information
Meeting: International Confernce on Advances in Patter Recognition
Location: Plymouth
Country: United Kingdom
Start Date: November 23, 1998
Distribution Limits
Public
Copyright
Other

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