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SSME Parameter Modeling with Neural Networks
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English
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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Dhawan, A., Wheeler, K., and Doniere, T., "SSME Parameter Modeling with Neural Networks," SAE Technical Paper 941221, 1994, https://doi.org/10.4271/941221.Also In
References
- Bickmore T. “Probabilistic Approach to Sensor Data Validation,” Tech. Rep. 92-3163 AIAA July 1992
- Lin T. G. C.S. Wu I.C. “Neural Networks for Sensor Failure Detection and Data Recovery,” Proceedings of International Conference on Artificial Neural Networks in Engineering St. Louis November 10-12 1991
- Makel D. Flaspohler W. Bickmore T. “Sensor Data Validation and Reconstruction, Phase 1: System Architecture Study,” Tech. Rep. CR 187122 NASA 1991
- Meyer C. M. Maul W. A. “The Application of Neural Networks to the SSME Startup Transient,” AIAA/SAE/ASME/ASEE 27th Joint Propulsion Conference 91-2530 Sacramento, CA June 1991
- Ruiz C. Hawman M. Galinaitis W. “Algorithms for Real-Time Fault Detection of the Space Shuttle Main Engine,” AIAA Paper 92-3167 1992
- Rumelhart D. E. Hinton G. E. Williams R. J. “Learning Internal Representations by Error Propagation,” Parallel Distributed Processing Explorations in the Microstructure of Cognition Rumelhart D. E. McClelland J. L. PDP Research Group 1 318 364 Cambridge, MA MIT Press 1986
- Cybenko G. “Approximation by Superpositions of a Sigmoidal Function.,” Mathematics of Control, Signals, and Systems 2 303 314 1989
- Hornik K. “Multilayer Feedforward Networks Are Universal Approximators,” Neural Networks 2 359 366 1989
- Funahashi K. “On the Approximate Realization of Continuous Mappings by Neural Networks.,” Neural Networks 2 183 192 1989
- Hertz J. Krogh A. Palmer R. G. Introduction to the Theory of Neural Computation Addison Wesley Publishing Company 1991
- Broomhead D. Lowe D. “Multivariable Functional Interpolation and Adaptive Networks,” Complex Systems 2 321 355 1988
- Powell M. “Radial Basis Functions for Multivariable Interpolation: A Review,” Algorithms for Approximation Mason J. Cox M. 143 167 Oxford Clarendon Press 1987
- Moody J. Darken C. “Learning with Localized Receptive Fields,” Proceedings of the 1988 Connectionist Models Summer School Touretzky D. Hinton G. Sejnowski T. san Mateo CA 133 143 Morgan Kaufmann Publishers 1988
- Moody J. Darken C. J. “Fast Learning in Networks of Locally-Tuned Processing Units,” Neural Computation 1 281 294 1989
- Park J. Sandberg I. W. “Universal Approximation Using Radial-Basis-Function Networks,” Neural Computation 3 246 257 Summer 1991
- Sun X. “On the Solvability of Radial Function Interpolation,” Approximation Theory VI Chui C. Schumaker L. Ward J. 2 643 646 Academic Press 1989
- Fahlman S. E. “An Empirical Study of Learning Speed in Back-Propagation Networks,” Technical Report CMU-CS-88-162 Carnegie Mellon University September 1988