An Auto-Weighting Aggregative Fuzzy Collaborative Intelligence Approach for DRAM Yield Forecasting

Hsin-Chieh Wu; Tin-Chih Toly Chen

doi:10.20944/preprints202108.0268.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 7 August 2021 / Approved: 11 August 2021 / Online: 11 August 2021 (18:08:46 CEST)

In a collaborative forecasting task, experts may have unequal authority levels. However, this has rarely been considered reasonably in the existing fuzzy collaborative forecasting methods. In addition, experts may not be willing to discriminate their authority levels. To address these issues, an auto-weighting fuzzy weighted intersection (FWI) fuzzy collaborative intelligence approach is proposed in this study. In the proposed auto-weighting FWI fuzzy collaborative intelligence approach, experts’ authority levels are automatically and reasonably assigned based on their past forecasting performances. Subsequently, the auto-weighting FWI mechanism is established to aggregate experts’ fuzzy forecasts. The theoretical properties of the auto-weighting FWI mechanism have been discussed and compared with those of the existing fuzzy aggregation operators. After applying the auto-weighting FWI fuzzy collaborative intelligence approach to a case of forecasting the yield of a DRAM product from the literature, its advantages over several existing methods were clearly illustrated.

Fuzzy collaborative intelligence; Dynamic random access memory; Fuzzy weighted intersection; Forecasting

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An Auto-Weighting Aggregative Fuzzy Collaborative Intelligence Approach for DRAM Yield Forecasting

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