A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy logic for the management of uncertainty
Distributed representation of fuzzy rules and its application to pattern classification
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Hybrid SOM-BPN Approach to Lot Output Time Prediction in a Wafer Fab
Neural Processing Letters
An intelligent hybrid system for wafer lot output time prediction
Advanced Engineering Informatics
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A hybrid intelligent approach is proposed which can be used to estimate the output of each product type in a semiconductor fabrication plant. This is a critical task for plant operation. First, the hybrid fuzzy-c-means (FCM) and fuzzy-back-propagation-neural-network (FBPN) approach is applied to estimate the output time for every job in the plant. Subsequently, the fuzzy output projection function (FOPF) is proposed to project the outputs into each future time period. To evaluate the advantages and/or disadvantages of the hybrid intelligent approach, a simulated semiconductor plant model is also used in this study to generate test data. From the experimental results, the output projection accuracy by the hybrid intelligent approach was significantly better than that of some existing approaches.