An auto-weighting FWI fuzzy collaborative intelligence approach for forecasting DRAM yield

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. The auto-weighting FWI fuzzy collaborative intelligence approach has been applied to a case of forecasting the yield of a DRAM product from the literature. The forecasting accuracy, measured in terms of mean absolute percentage error, was considerably enhanced. The advantage over existing methods was significant after conducting a paired t test.