Application of FCM Clustering Based Rough Sets on Steel Rolling Process
This paper presents a model for predicting steel mechanical property based on rough sets and fuzzy c means clustering. Rough sets is an intelligent method, which can only be applied to data table with discrete attributes. However the practical data set is normally continuous, and rough sets cannot be used directly. FCM clustering is used to transform the continuous attributes to discretized ones and a discretized decision table can be got. Rough sets reduce the discretized decision table to discover significant attributes of a data set and filter out those attributes which are unimportant. Finally, to verify the validity of the proposed method, it is used for practical data acquired from some steel works, and the simulation results show that the attribute reduction contains the same information as the original one.
Rough Sets Fuzzy C-means Clustering Discretization Attribute Reduction Steel Rolling Process
Li Wang Xianzhong Zhou Guangming Zhang
School of Engineering and Management, Nanjing University, Nanjing 210093, China School of Automation School of Engineering and Management, Nanjing University, Nanjing 210093, China School of Automation and Electrical Engineering, Nanjing University of Technology, Nanjing 210009, C
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
512-516
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）