This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB®.
Feature Based Cost Modeling is thought of as a relative new approach to cost modeling, but feature based cost modeling had considerable development in the 1950's. Considerable work was published in the 1950's by Boeing on cost for various casting processes--sand casting, die casting, investment casting and permanent mold casting--as a function of a single casting feature, casting volume. Additional approaches to feature based cost modeling have been made, and this work is a review of previous works and a proposed integrated model to feature based cost modeling.
Conference Committee Involvement (2)
Intelligent Systems in Design and Manufacturing V
25 October 2004 | Philadelphia, Pennsylvania, United States