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- W160348976 abstract "Recent research efforts have investigated the use of competitive neural networks to classify the Mach number responses of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton Virginia. Control input and Mach-number response data from a 45 minute tunnel run were used for training and testing the neural network classifiers. The wind tunnel Mach number was varied from 0.4 to 1.3, covering most of the operational range of facility. Control inputs, consisting of raise or lower commands of varying duration to the tunnel fan drive system, ranged from 0.25 seconds to 2.5 minutes. Prediction of the Mach number response for a specific class of responses was investigated using a recursive, gamma-memory neural network topology suited to system identification. The combination of classification using a competitive network, and prediction using a gamma network, provides increased accuracy in predicting the tunnels''s Mach number response to a specific class of control inputs." @default.
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- W160348976 date "1995-07-01" @default.
- W160348976 modified "2023-09-26" @default.
- W160348976 title "Classification and Prediction of Wind Tunnel Mach Number Responses Using Both Competitive and Gamma Neural Networks" @default.
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