A selectively fuzzified back propagation network approach for precisely estimating the cycle time range in wafer fabrication

Yu Cheng Wang, Horng Ren Tsai, Toly Chen*

*此作品的通信作者

研究成果: Article同行評審

13 引文 斯高帕斯(Scopus)

摘要

Forecasting the cycle time of each job is a critical task for a factory. However, recent studies have shown that it is a challenging task, even with state-of-the-art deep learning techniques. To address this challenge, a selectively fuzzified back propagation network (SFBPN) approach is proposed to estimate the range of a cycle time, the results of which provide valuable information for many managerial purposes. The SFBPN approach is distinct from existing methods, because the thresholds on both the hidden and output layers of a back propagation network are fuzzified to tighten the range of a cycle time, while most of the existing methods only fuzzify the threshold on the output node. In addition, a random search and local optimization algorithm is also proposed to derive the optimal values of the fuzzy thresholds. The proposed methodology is applied to a real case from the literature. The experimental results show that the proposed methodology improved the forecasting precision by up to 65%.

原文English
文章編號1430
期刊Mathematics
9
發行號12
DOIs
出版狀態Published - 2 6月 2021

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