| Abstrakt: |
Accurate ORCT prediction was helpful to adjust production schedule and optimize manufacturing processes dynamically, which ensured timely orders delivery. ORCT affected by various production factors, including materials, equipment, works-in-process, et al. The related data possessed typical characteristics of large-scale, multi-dimensions and high-redundancy. Effective feature election might improve the prediction accuracy. On the basis of constructing candidate feature sets, a feature selection algorithm was proposed based on SOM-FWFCM algorithm. Firstly, the cluster centers of FWFCM algorithm were initialized by SOM network to reduce the reliance on initial cluster centers. Feature weights were calculated by mutual information to achieve feature clustering with guidance. Then, according to the cluster results, representational features were selected to build high-quality key feature subset. Finally, taking the production data of a machining shop as an example, the effectiveness of the proposed algorithm was verified by comparing with other four feature selection algorithms. [ABSTRACT FROM AUTHOR] |
