Enhancement of Prediction for Manufacturing System Using Bayesian Decision Recognition (特集:生産・流通システム高度情報化技術)

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概要

• 論文の詳細を見る

• 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.

• 社団法人 電気学会の論文
• 2004-01-01

著者

• Fujimoto Yasutaka

  Dept. Of Electrical & Computer Engineering Yokohama National University

• YANG Jianhua

  Dept. of Electrical & Computer Engineering, Yokohama National University

• JIANHUA Yang

  Dept. of Electrical & Computer Engineering, Yokohama National University

• Jianhua Yang

  Dept. Of Electrical & Computer Engineering Yokohama National University

• Fujimoto Yasutaka

  Dept. of Electrical & Computer Eng., Yokohama National University

関連論文

• On Approximation and Smoothing of General Distribution Based on Normal Expansion Method (特集:平成15年電気学会電子・情報・システム部門大会)
• Dynamic Scheduling of Large-scale Flow Shops Based on Relative Priority Approach
• Enhancement of Prediction for Manufacturing System Using Bayesian Decision Recognition (特集:生産・流通システム高度情報化技術)

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