A hybrid fuzzy and neural approach for DRAM price forecasting

Abstract

The trend in the price of dynamic random access memory (DRAM) is a very important prosperity index in the semiconductor industry. To further enhance the performance of DRAM price forecasting, a hybrid fuzzy and neural approach is proposed in this study. In the proposed approach, multiple experts construct their own fuzzy multiple linear regression models from various viewpoints to forecast the price of a DRAM product. Each fuzzy multiple linear regression model can be converted into two equivalent nonlinear programming problems to be solved. To aggregate these fuzzy price forecasts, a two-step aggregation mechanism is applied. At the first step, fuzzy intersection is applied to aggregate the fuzzy price forecasts into a polygon-shaped fuzzy number, in order to improve the precision. After that, a back propagation network is constructed to defuzzify the polygon-shaped fuzzy number and to generate a representative/crisp value, so as to enhance the accuracy. A real example is used to evaluate the effectiveness of the proposed methodology. According to experimental results, the proposed methodology improved both the precision and accuracy of DRAM price forecasting by 66% and 43%, respectively.