Possibilistic linear systems and their application to the linear regression model
Fuzzy Sets and Systems
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy linear regression with fuzzy intervals
Fuzzy Sets and Systems
An efficient approach for large scale project planning based on fuzzy Delphi method
Fuzzy Sets and Systems
Fuzzy Mathematical Models in Engineering and Management Science
Fuzzy Mathematical Models in Engineering and Management Science
An intelligent hybrid system for wafer lot output time prediction
Advanced Engineering Informatics
An intelligent mechanism for lot output time prediction and achievability evaluation in a wafer fab
Computers and Industrial Engineering
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
Mathematical and Computer Modelling: An International Journal
Applying a Fuzzy and Neural Approach for Forecasting the Foreign Exchange Rate
International Journal of Fuzzy System Applications
International Journal of Fuzzy System Applications
International Journal of Fuzzy System Applications
A collaborative and artificial intelligence approach for semiconductor cost forecasting
Computers and Industrial Engineering
Computers and Industrial Engineering
A fuzzy-neural approach for global CO2 concentration forecasting
Intelligent Data Analysis
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To further enhance the performance of job completion time prediction and internal due date assignment in a wafer fab, a fuzzy-neural knowledge-based system is constructed in this study. In the constructed system, multiple experts construct their own fuzzy multiple linear regression models from various viewpoints to predict the completion/cycle time of a job. Each fuzzy multiple linear regression model can be converted into an equivalent non-linear programming problem to be solved. Subsequently, a two-step aggregation mechanism is applied. At the first step, fuzzy intersection is applied to aggregate the fuzzy completion time forecasts into a polygon-shaped fuzzy number, in order to improve the precision of completion time forecasting. The polygon-shaped fuzzy number contains the actual value, and its upper bound determines the internal due date of the job. After that, a back propagation network is constructed to defuzzify the polygon-shaped fuzzy number and to generate a representative/crisp value, so as to enhance the accuracy. A practical example is used to evaluate the effectiveness of the proposed methodology. According to experimental results, the proposed methodology improved both the precision and accuracy of job cycle time prediction by 16 and 21%, respectively.