华南理工大学学报(自然科学版) ›› 2011, Vol. 39 ›› Issue (8): 36-41.doi: 10.3969/j.issn.1000-565X.2011.08.007

• 机械工程 • 上一篇    下一篇

制造决策的知识融合粗糙集模型

徐晓 翟敬梅刘海涛 康博   

  1. 华南理工大学 机械与汽车工程学院,广东 广州 510640
  • 收稿日期:2011-02-24 修回日期:2011-04-13 出版日期:2011-08-25 发布日期:2011-07-06
  • 通信作者: 翟敬梅(1967-),女,副教授,主要从事机械设计、生产过程建模和优化等研究 E-mail: mejmzhai@scut.edu.cn E-mail:simonxx@ scut.edu.cn
  • 作者简介:徐晓(1963-) ,男,讲师,主要从事机械设计、机械CAD 和人工智能等研究.
  • 基金资助:

    国家“863”计划项目( 2009AA043901)

A Knowledge-Based Rough Set Model for Manufacturing Decision

Xu Xiao  Zhai Jing-mei  Liu Hai-tao  Kang Bo   

  1. School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China
  • Received:2011-02-24 Revised:2011-04-13 Online:2011-08-25 Published:2011-07-06
  • Contact: 翟敬梅(1967-),女,副教授,主要从事机械设计、生产过程建模和优化等研究.E-mail: mejmzhai@scut.edu.cn E-mail:simonxx@ scut.edu.cn
  • About author:徐晓(1963-) ,男,讲师,主要从事机械设计、机械CAD 和人工智能等研究.
  • Supported by:

    国家“863”计划项目( 2009AA043901)

摘要: 由于制造系统的复杂性和不确定性,单一的知识建模或数据挖掘建模都面临着知识或数据信息的不完备.为有效、充分地利用已有信息减少不确定性,文中提出了知识和数据挖掘相融合的建模思想,将知识嵌入到粗糙集模型中,建立了知识的函数关系,给出了基于不可分辨-函数关系的粗糙集决策模型,研究了不可分辨- 函数关系下的知识分类和推理.相比原粗糙集模型,基于知识的粗糙集模型具有更高的划分精度,发现知识更丰富,结构形式更具归纳性.实验结果验证了决策模型的有效性和应用的灵活性.

关键词: 决策, 知识融合, 粗糙集, 不可分辨-函数关系

Abstract:

Due to the complexity and uncertainties of the manufacturing system,both the knowledge modeling and the data mining modeling are restricted by incomplete knowledge and data. In this paper,in order to reduce the uncertainties by making full and effective use of the known information,an modeling idea of integrating the knowledge with the data mining is proposed to embed the knowledge into the rough set model,with the function relations of the knowledge established. Then,based on the indiscernibility relations and the function relations,a novel knowledgebased rough set model ( KBRSM) is developed and the knowledge classification and inference are investigated. As compared with the original rough set model,KBRSM is of high classification accuracy,excellent performance of knowledge discovery as well as of a generalized form. Experimental results show that the proposed decision model is effective and practically flexible.

Key words: decision, knowledge fusion, rough set, indiscernibility-function relation

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