A hybrid neural network model in handwritten word recognition
Neural Networks
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
SOM Ensemble-Based Image Segmentation
Neural Processing Letters
An intelligent mechanism for lot output time prediction and achievability evaluation in a wafer fab
Computers and Industrial Engineering
A hybrid intelligent approach for output projection in a semiconductor fabrication plant
Intelligent Data Analysis
Computers and Industrial Engineering
International Journal of Fuzzy System Applications
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Output time prediction is a critical task to a wafer fab (fabrication plant). To further enhance the accuracy of wafer lot output time prediction, the concept of input classification is applied to the back propagation network (BPN) approach in this study by pre-classifying input examples with the self-organization map (SOM) classifier before they are fed into the BPN. Examples belonging to different categories are then learned with different BPNs but with the same topology. Production simulation is also applied in this study to generate test examples. According to experimental results, the prediction accuracy of the proposed methodology was significantly better than those of three existing approaches, case-based reasoning (CBR), BPN without example classification, and evolving fuzzy rules (EFR), in most cases by achieving a 13---46% (and an average of 30%) reduction in the root-mean-squared-error (RMSE) over the comparison basis --- BPN without example classification.