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SSME parameter estimation using radial basis function neural networks

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2 Author(s)
Wheeler, K.R. ; Dept. of Electr. & Comput. Eng., Cincinnati Univ., OH, USA ; Dhawan, Atam P.

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

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:5 )

Date of Conference:

27 Jun-2 Jul 1994