A Fuzzy/Neural Approach to Cost Prediction with Small Data Sets

• Main Author: Danker-McDermot, Holly
• Format: Others
• Published: ScholarWorks@UNO 2004
• Subjects: neural; fuzzy
• Online Access: http://scholarworks.uno.edu/td/86; http://scholarworks.uno.edu/cgi/viewcontent.cgi?article=1085&context=td

The project objective in this work is to create an accurate cost estimate for NASA engine tests at the John C. Stennis Space Center testing facilities using various combinations of fuzzy and neural systems. The data set available for this cost prediction problem consists of variables such as test duration, thrust, and many other similar quantities, unfortunately it is small and incomplete. The first method implemented to perform this cost estimate uses the locally linear embedding (LLE) algorithm for a nonlinear reduction method that is then put through an adaptive network based fuzzy inference system (ANFIS). The second method is a two stage system that uses various ANFIS with either single or multiple inputs for a cost estimate whose outputs are then put through a backpropagation trained neural network for the final cost prediction. Finally, method 3 uses a radial basis function network (RBFN) to predict the engine test cost.