SSME parameter estimation using radial basis function neural networks

Kevin R. Wheeler, Atam Dhawan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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)
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherIEEE
Pages3352-3357
Number of pages6
Volume5
StatePublished - Dec 1 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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