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

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

*此作品的通信作者

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

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).

原文English
文章編號100186
期刊Decision Analytics Journal
6
DOIs
出版狀態Published - 3月 2023

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