Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
3 Author(s)
Peck, C.C. ; Dept. of Electr. & Comput. Eng., Cincinnati Univ., OH, USA ; Dhawan, Atam P. ; Meyer, C.M.

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 if unknown and complex and the number of possible input parameters is quite large. Due to the optimization and space searching capabilities of genetic algorithms, they are employed 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 provided

Published in:

Neural Networks, 1993., IEEE International Conference on

Date of Conference: