A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Audio signal segmentation and classification using fuzzy c-means clustering
Systems and Computers in Japan
A Hybrid SOM-BPN Approach to Lot Output Time Prediction in a Wafer Fab
Neural Processing Letters
A look-ahead fuzzy back propagation network for lot output time series prediction in a wafer fab
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
A Method of Face Recognition Based on Fuzzy c-Means Clustering and Associated Sub-NNs
IEEE Transactions on Neural Networks
Using neural networks and data mining techniques for the financial distress prediction model
Expert Systems with Applications: An International Journal
Applying text and data mining techniques to forecasting the trend of petitions filed to e-People
Expert Systems with Applications: An International Journal
Colour image segmentation using fuzzy clustering techniques and competitive neural network
Applied Soft Computing
A PCA-FBPN Approach for Job Cycle Time Estimation in a Wafer Fabrication Factory
International Journal of Fuzzy System Applications
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Job completion time prediction is a critical task to a semiconductor fabrication factory. To further enhance the effectiveness/accuracy of job completion time prediction in a semiconductor fabrication factory, a hybrid fuzzy c-means (FCM) and back propagation network (BPN) approach is proposed in this study. In the proposed FCM-BPN approach, input examples are firstly pre-classified with FCM before they are fed into the BPN. Then, examples belonging to different categories are learned with different BPNs but with the same topology. After learning, these BPNs form a BPN ensemble that can be applied to predict the completion time of a new job. The output of the BPN ensemble is derived by aggregating the outputs from the component BPNs with another BPN and determines the completion time forecast. To validate the effectiveness of the proposed methodology and to make comparison with some existing approaches, the actual data in a semiconductor fabrication factory were collected. According to experimental results, the prediction accuracy of the proposed methodology was significantly better than those of some existing approaches. Besides, applying the fuzzy set theory was shown to be very effective in forming job categories and in deriving a representative value from the BPN ensemble. Both contributed to the superiority of the proposed methodology.