SSME Parameter Modeling with Neural Networks

Paper #:
  • 941221

Published:
  • 1994-04-01
Citation:
Dhawan, A., Wheeler, K., and Doniere, T., "SSME Parameter Modeling with Neural Networks," SAE Technical Paper 941221, 1994, https://doi.org/10.4271/941221.
Pages:
9
Abstract:
The High Pressure Oxidizer Turbine (HPOT) discharge temperature of the Space Shuttle Main Engine (SSME) was estimated using Radial Basis Function Neural Networks (RBFNN) during the startup transient. Estimation was performed for both nominal engine operation and during simulated input sensor failures. The K-means clustering algorithm was used on the data to determine the location of the basis function centers. The performance of the RBFNN is compared with that of a feedforward neural network trained with the Quickprop learning algorithm.
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