A collaborative fuzzy-neural approach for forecasting the price of a DRAM product

Tin-Chih Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Pages (from-to)95-109
Number of pages15
JournalInternational Journal of Technology Intelligence and Planning
Issue number2
StatePublished - 1 Oct 2011


  • Collaborative
  • DRAM
  • Dynamic random access memory
  • Forecasting
  • Fuzzy
  • Neural
  • Price

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