模糊数据样本统计中的联合扩张问题
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  • 英文篇名:Joint Expansion Problem in Fuzzy Data Sample Statistics
  • 作者:郭嗣琮 ; 赵小倩
  • 英文作者:GUO Si-zong;ZHAO Xiao-qian;Institute of Intelligent Engineering and Mathematics,Liaoning Technical University;
  • 关键词:模糊结构元 ; 模糊统计量
  • 英文关键词:Fuzzy Structured Element;;Fuzzy Statistics
  • 中文刊名:MUTE
  • 英文刊名:Fuzzy Systems and Mathematics
  • 机构:辽宁工程技术大学智能工程与数学研究院;
  • 出版日期:2019-06-15
  • 出版单位:模糊系统与数学
  • 年:2019
  • 期:v.33;No.140
  • 语种:中文;
  • 页:MUTE201903010
  • 页数:9
  • CN:03
  • ISSN:43-1179/O1
  • 分类号:102-110
摘要
在大数据的背景下,运用模糊数据统计分析的理论与方法越来越受到重视。本文论述了模糊数据统计中存在的自然扩张和联合扩张问题,指出了统计量自然扩张的错误并给出了联合扩张的解决方案,利用模糊数的结构元表达方法构建了几个重要样本公式,它们是模糊数据统计分析的重要工具。
        In the context of big data,theories and methods using statistical analysis of fuzzy data are receiving more and more attention. This paper discusses the problems of natural expansion and joint expansion in fuzzy data statistics,points out the error of natural expansion of statistics and gives the solution of joint expansion.Several important sample formulas are constructed by using the structural element expression method of fuzzy numbers. They are an important tool for statistical analysis of fuzzy data.
引文
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