A novel auto-weighting deep-learning fuzzy collaborative intelligence approach

Yu Cheng Wang*, Tin Chih Toly Chen, Hsin Chieh Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Experts often have unequal authority levels in organizations. However, this has rarely been considered reasonably in solving problems in the manufacturing system. In addition, a fuzzy collaborative estimation method can be more flexible and effective if experts have unequal authority levels. We propose an auto-weighting deep-learning fuzzy collaborative intelligence approach to address these issues. The experts’ authority levels are automatically assigned based on their past performances in the proposed auto-weighting deep-learning fuzzy collaborative intelligence approach. The fuzzy weighted intersection (FWI) is applied to aggregate experts’ fuzzy estimates based on the assigned authority levels. Subsequently, a deep neural network (DNN) is constructed to defuzzify the aggregation result. We then apply the auto-weighting deep-learning fuzzy collaborative intelligence approach to a case of estimating the unit cost of a dynamic random access memory (DRAM) product. We show the advantage of the proposed approach over several existing methods using mean absolute percentage error (MAPE).

Original languageEnglish
Article number100186
JournalDecision Analytics Journal
Volume6
DOIs
StatePublished - Mar 2023

Keywords

  • Deep neural network
  • Dynamic random access memory
  • Estimation
  • Fuzzy collaborative intelligence
  • Fuzzy weighted intersection
  • Unit cost

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