SSME parameter estimation using radial basis function neural networks

Kevin R. Wheeler; Atam P. Dhawan

SSME parameter estimation using radial basis function neural networks

Research output: Contribution to conference › Paper › peer-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 language	English (US)
Pages	3352-3357
Number of pages	6
State	Published - 1994
Externally published	Yes
Event	Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA Duration: Jun 27 1994 → Jun 29 1994

Other

Other	Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
City	Orlando, FL, USA
Period	6/27/94 → 6/29/94

All Science Journal Classification (ASJC) codes

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title = "SSME parameter estimation using radial basis function neural networks",

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.",

author = "Wheeler, {Kevin R.} and Dhawan, {Atam P.}",

year = "1994",

language = "English (US)",

pages = "3352--3357",

note = "Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) ; Conference date: 27-06-1994 Through 29-06-1994",

}

TY - CONF

T1 - SSME parameter estimation using radial basis function neural networks

AU - Wheeler, Kevin R.

AU - Dhawan, Atam P.

PY - 1994

Y1 - 1994

N2 - 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.

AB - 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.

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M3 - Paper

AN - SCOPUS:0028754467

SP - 3352

EP - 3357

T2 - Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)

Y2 - 27 June 1994 through 29 June 1994

ER -

SSME parameter estimation using radial basis function neural networks

Abstract

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All Science Journal Classification (ASJC) codes

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