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.
|頁（從 - 到）||1013-1025|
|期刊||Journal of Ambient Intelligence and Humanized Computing|
|出版狀態||Published - 1 八月 2018|