SSME parameter estimation using radial basis function neural networks

Kevin R. Wheeler, Atam P. Dhawan

Research output: Contribution to conferencePaperpeer-review

Abstract

Radial Basis Function Neural Networks (RBFNN) were used to estimate Space Shuttle Main Engine (SSME) sensor values for sensor validation. The High Pressure Oxidizer Turbine (HPOT) discharge temperature, a redlined parameter, was estimated during the startup transient of nominal engine operation and during simulated input sensor failures. The K-Means clustering algorithm was used on the data for placement 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)
Pages3352-3357
Number of pages6
StatePublished - 1994
Externally publishedYes
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: Jun 27 1994Jun 29 1994

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period6/27/946/29/94

All Science Journal Classification (ASJC) codes

  • Software

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