Title :
SSME parameter estimation using radial basis function neural networks
Author :
Wheeler, Kevin R. ; Dhawan, Atam P.
Author_Institution :
Dept. of Electr. & Comput. Eng., Cincinnati Univ., OH, USA
fDate :
27 Jun-2 Jul 1994
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
Keywords :
aerospace computing; aerospace engines; feedforward neural nets; parameter estimation; sensors; space vehicles; K-means clustering; Quickprop learning algorithm; Space Shuttle main engine; discharge temperature; feedforward neural network; high pressure oxidizer turbine; input sensor failures; parameter estimation; radial basis function neural networks; redlined parameter; sensor validation; startup transient; Artificial neural networks; Clustering algorithms; Engines; Feedforward neural networks; Neural networks; Parameter estimation; Radial basis function networks; Space shuttles; Temperature sensors; Turbines;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374774
