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

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