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A decision model stemmed from Bayesian theorem is proposed to describe the process of decision making for job sequence in manufacturing system. The construction of feature vector is firstly discussed with respect to the manufacturing system’s characteristic. Then a non-parametric model is employed to deal with general distribution for decision acquisition, where a binary division methodology is developed to limit the size of non-parametric model, including elimination of irrelevant features. At last, a PCB manufacturing system is given to demonstrate the efficiency of the model.

Enhancement of Prediction for Manufacturing System Using Bayesian Decision Recognition