Version 1
: Received: 7 August 2021 / Approved: 11 August 2021 / Online: 11 August 2021 (18:08:46 CEST)
How to cite:
Wu, H.; Chen, T.T. An Auto-Weighting Aggregative Fuzzy Collaborative Intelligence Approach for DRAM Yield Forecasting. Preprints2021, 2021080268. https://doi.org/10.20944/preprints202108.0268.v1
Wu, H.; Chen, T.T. An Auto-Weighting Aggregative Fuzzy Collaborative Intelligence Approach for DRAM Yield Forecasting. Preprints 2021, 2021080268. https://doi.org/10.20944/preprints202108.0268.v1
Wu, H.; Chen, T.T. An Auto-Weighting Aggregative Fuzzy Collaborative Intelligence Approach for DRAM Yield Forecasting. Preprints2021, 2021080268. https://doi.org/10.20944/preprints202108.0268.v1
APA Style
Wu, H., & Chen, T.T. (2021). An Auto-Weighting Aggregative Fuzzy Collaborative Intelligence Approach for DRAM Yield Forecasting. Preprints. https://doi.org/10.20944/preprints202108.0268.v1
Chicago/Turabian Style
Wu, H. and Tin-Chih Toly Chen. 2021 "An Auto-Weighting Aggregative Fuzzy Collaborative Intelligence Approach for DRAM Yield Forecasting" Preprints. https://doi.org/10.20944/preprints202108.0268.v1
Abstract
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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
