期刊文献+

逐步回归分析在锻模飞边尺寸设计准则挖掘中的应用 被引量:1

Application of the Step-by-Step Regressive Analysis to the Design Criterion Mining of Forging-Die Flash Sizes
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摘要 分析了采用传统方法建立锻模飞边尺寸设计准则存在的问题,提出了采用逐步回归分析法挖掘锻模飞边尺寸设计准则的思想,构造了利用逐步回归分析法对锻模飞边尺寸设计准则进行参数估计的算法;对逐步回归分析软件的需求分析与结构设计及该软件中数据的分析与设计进行了论述;以此为依据开发了该软件,并给出了利用该软件获得的锻模飞边尺寸设计准则.结果表明,利用由此得到的设计准则而开发的CAD系统具有自组织能力和自适应能力. Some problems in setting up forging-die flash size design criteria by means of traditional methods were analyzed. The idea of applying the step-by-step regressive analysis method to mining forging-die flash size design criteria was pointed out and the algorithm of estimating the parameters in forging-die flash size design criteria was constructed. The requirement analysis and structure design of the step-by-step regressive analysis software, and the data analysis and design within the software were expounded. According to these ideas, the software was developed. The design criteria of forging-die flash sizes with the software used were given. The results showed that the CAD systems developed using these criteria had self-assemble ability and self-adaptive ability.
作者 谭建豪 章兢
出处 《湖南大学学报(自然科学版)》 EI CAS CSCD 北大核心 2006年第4期55-59,共5页 Journal of Hunan University:Natural Sciences
基金 教育部科学技术研究重点项目(教技司[2001]224号)
关键词 数据挖掘 回归分析 参数估计 data mining regression analysis parameter estimation
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参考文献8

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二级参考文献4

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共引文献7

同被引文献8

  • 1张树有,纪杨建,谭建荣,彭群生.非关联尺寸标注干涉的自适应处理[J].浙江大学学报(工学版),2001,35(6):676-680. 被引量:8
  • 2TEREPUH ГП,ПOXUHП U.热体积模锻工艺过程设计最优化和自动化原理[M].肖景容,李德群,译.北京:国防工业出版社,1983.
  • 3BUNTINE W L. Operations for learning with graphical models [J ]. Journal of Artifical Intelligence Research , 1994, 2 : 159 - 225.
  • 4CHU W W, CHEN Q. Neighborhood and associative query answering[J]. Journal of Intelligence Information Systems, 1992, 1:355 - 382.
  • 5STONE M. Cross-validatory choice and assessment of statistical and predications [J ]. Journal of the Royal Statistical Society, 1974, 36:111- 147.
  • 6WANG R, STOREY V, FIRTH C. A framework for analysis of data quality research[J ]. IEEE Transactions on Knowledge and Data Engineering, 1995, 7:623 - 640.
  • 7STONE M. Classification and regression trees[J ]. Wadsworth International Group, 1984,8:452 - 456.
  • 8RAKESH A, RAMAKRISHNAN S. Fast algorithms for mining association rules in large database[C]//Proceedings of the Twentieth International Conference on Very Large Databases. Santiago, Chile, 1994:540- 544.

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