This paper presents a collaborative fuzzy-neural approach for accurately and precisely forecasting the price of a Dynamic Random Access Memory (DRAM) product, which is considered as one of the most important semiconductors widely used in various applications. In the collaborative fuzzy-neural approach, some experts from the application domain are invited to form a council. For some aspects, these experts put forward different points of view. These views are incorporated into the Back Propagation Network and Nonlinear Programming (BPN-NP) approach, and result in different fuzzy-valued price forecasts. To derive a single representative value from these fuzzy price forecasts, the Fuzzy Intersection and Radial Basis Function network (FI-RBF) approach is employed. The effectiveness of the collaborative fuzzy-neural approach is validated with a real example containing the 256-day price data of a 1G DRAM product.
|Number of pages||15|
|Journal||International Journal of Technology Intelligence and Planning|
|State||Published - 1 Oct 2011|
- Dynamic random access memory