期刊文献+

粗糙集理论及其在机电行业中的应用潜力分析 被引量:3

Rough Set Theory and It' s Application Potential Analysis in Mechatronics Industry
下载PDF
导出
摘要 介绍了粗糙集理论的基本概念和计算方法,综述了粗糙集理论的特点和研究现状。 分析机电行业的生产特点,首次将粗糙集理论引入到机电行业中,对于粗糙集理论在机电产品的市 场定位、粗糙控制、设计知识发现、故障诊断等问题的应用潜力和应用途径进行了分析和研究。通 过产品装配知识发现的实例,验证了粗糙集理论的有效性和实用性,为机电行业中传统的决策和控 制问题提出了新的解决思路。 In this paper, the basic concepts and computation methods of rough set theory are introduced. Rough set theory' s characteristics and researching status are analyzed. With the production analysis of the mechatronics industry, for the first time, the rough set theory is applied in the production of mechatronics products. Especially the applying potential and general solution with rough set for product's market location analysis, rough controlling of the products, knowledge discovery of the product design, fault diagnosis, etc. are presented. A application example of the assemble knowledge discovery shows the solution with rough set theory. And by this approach, a new solution idea is put forward for the traditional decision- making and controlling in the mechatronics industry.
出处 《机电一体化》 2005年第6期24-26,共3页 Mechatronics
基金 获中德政府合作项目(NO.20002DFG00027)支持
关键词 粗糙集 机电行业 机电产品 知识发现 规则生成 rough set mechatronics industry mechatronics product knowledge discovery rule generation
  • 相关文献

参考文献6

  • 1Pawlak Z. Rough set [ J]. International Jounal of Information and Computer Science. 1982,11 (5) ,341 ~ 356.
  • 2Pawlak, Zdzislaw. Rough sets and Interlligent Data Analysis[J].Information Sciences Volume: 147, Issue: 1~ 4, November,2002,1 ~12.
  • 3Pawlak, Zdzislaw. Decisions Rules and Flow Networks [ J ].European Journal of Operational Research Volume: 154, Issue:1, April 1, 2004, 184 ~ 190.
  • 4赵荣泳.[D].上海:同济大学出版社,2005.
  • 5Ewa Orlowska. Incomplete Information: Rough Set Analysis [ M ].Physica - Verlag Heidelberg. New York, 1998.
  • 6Lech Polkowski, Shusaku Tsumoto, Tsau Y Lin. Rough Set Methods and Applications [ M ]. Physica - Verlag Heidelberg.New York, 2000.

共引文献3

同被引文献16

  • 1赵荣泳,张浩,张辉,樊留群,陆剑峰.一种新的机器学习方法——PSVM应用于数控磨床智能诊断的研究[J].制造业自动化,2005,27(1):42-46. 被引量:1
  • 2Kimball R, Ross M. The data warehouse toolkit: The complete guide to dimensional modeling[M]. 2nd ed. New York, NY, USA: John Wiley & Sons, 2002.
  • 3Vapnik V N. The nature of statistical learning theory[M]. 3rd ed. New York, NY, USA: Springer, 2005.
  • 4Chang C C, Lin C J. LIBSVM - A library for support vector machines[EB/OL]. [2002-12-04]. http://www.csie.ntu. edu.tw/-cj lin/libsvm.
  • 5Giovinazzo W A. Object-oriented datawarehouse design: Building a star schema [M]//Upper Saddle River, NJ, USA: Prentice-Hall, 2000.
  • 6Witten I H, Frank E. Data mining: Practical machine learning tools and techniques[M]. 2rid ed. San Francisco, CA, USA: Morgan Kaufmann, 2006.
  • 7刘清.Rough集及Rough推理[M].北京:科学出版社,2001..
  • 8Pawlak,Z.Rough sets[J].International Journal of Computer and Information Sciences,1982,11 (5):341-356.
  • 9Pawlak,Z.Rough sets theory and its applications to data analysis[J].Cybernetics and Systems,1998(29):661-668.
  • 10Vinterbo S,φhrn A.Minimal approximate hitting sets and rule templates[J].International Journal of Approximate Reasoning,2000,25(2):123-143.

引证文献3

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部