Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

Charles C. Peck, Atam P. Dhawan, Claudia M. Meyer

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

7 Scopus citations

Abstract

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the Space Shuttle Main Engine, the functional relationships among measured parameters is unknown and complex. Further-more, the number of possible input parameters is quite large. Many approaches have been proposed for input selection, but they are either not possible due to insufficient instrumentation, are subjective, or they do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms, they were employed in this study to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

Original languageEnglish (US)
Title of host publication1993 IEEE International Conference on Neural Networks, ICNN 1993
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1115-1122
Number of pages8
ISBN (Electronic)0780309995
DOIs
StatePublished - 1993
Externally publishedYes
EventIEEE International Conference on Neural Networks, ICNN 1993 - San Francisco, United States
Duration: Mar 28 1993Apr 1 1993

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume1993-January
ISSN (Print)1098-7576

Other

OtherIEEE International Conference on Neural Networks, ICNN 1993
CountryUnited States
CitySan Francisco
Period3/28/934/1/93

All Science Journal Classification (ASJC) codes

  • Software

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