SSME parameter modeling with neural networks

Atam Dhawan, Kevin R. Wheeler, Timothy F. Doniere

Research output: Contribution to conferencePaperpeer-review

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.

Original languageEnglish (US)
DOIs
StatePublished - Dec 1 1994
Externally publishedYes
EventAerospace Atlantic Conference and Exposition - Dayton, OH, United States
Duration: Apr 18 1994Apr 22 1994

Other

OtherAerospace Atlantic Conference and Exposition
CountryUnited States
CityDayton, OH
Period4/18/944/22/94

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Pollution
  • Industrial and Manufacturing Engineering

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