An innovative fuzzy and artificial neural network approach for forecasting yield under an uncertain learning environment

Tin-Chih Chen*

*此作品的通信作者

研究成果: Article同行評審

11 引文 斯高帕斯(Scopus)

摘要

Most methods for fitting an uncertain yield learning process involve using fuzzy logic and solving mathematical programming (MP) problems, and thus have several drawbacks. The present study proposed a novel fuzzy and artificial neural network (ANN) approach for overcoming these drawbacks. In the proposed methodology, an ANN is used instead of an MP model to facilitate generating feasible solutions. A two-stage procedure is established to train the ANN. The proposed methodology and several existing methods were applied to a real case in a semiconductor manufacturing factory, and the experimental results showed that the methodology outperformed the existing methods in the overall forecasting performance.

原文English
頁(從 - 到)1013-1025
頁數13
期刊Journal of Ambient Intelligence and Humanized Computing
9
發行號4
DOIs
出版狀態Published - 1 八月 2018

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