[1]李雪.一种基于正态分布密度函数的模糊查询方[J].计算机技术与发展,2018,28(09):1-6.[doi:10.3969/j.issn.1673-629X.2018.09.001]
　LI Xue.A Fuzzy Matching Method Based on Normal Distribution Density Function[J].,2018,28(09):1-6.[doi:10.3969/j.issn.1673-629X.2018.09.001]
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一种基于正态分布密度函数的模糊查询方()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年09期
页码:
1-6
栏目:
智能、算法、系统工程
出版日期:
2018-09-10

文章信息/Info

Title:
A Fuzzy Matching Method Based on Normal Distribution Density Function
文章编号:
1673-629X(2018)09-0001-06
作者:
李雪
南京航空航天大学 计算机科学与技术学院,江苏 南京,211100
Author(s):
LI Xue
School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics, Nanjing 211100,China
关键词:
模糊查询;  大样本数据;  正态分布;  密度函数;  隶属函数;  模糊集合;  区间匹配
Keywords:
fuzzy query; large sample data; normal distribution; density function; membership function; fuzzy set; interval matching
分类号:
TP301
DOI:
10.3969/j.issn.1673-629X.2018.09.001
文献标志码:
A
摘要:
在数据库中进行信息查询时,用户经常需要表示不精确的查询请求.然而传统的数据库无法对这些不精确的查询条件进行匹配,导致查询到空的结果集或者查询结果过多从而难以筛选,难以满足用户实际要求.在关系数据库中进行模糊查询已经进行了大量研究,其中大部分是对不同的模糊集设置不同的隶属函数来进行查询,将其应用于大样本数据时便会遇到很多困难.根据模糊集理论以及正态分布函数适合于大样本数据的特征,文中用正态分布密度函数来一般化隶属函数,使其可以自动对模糊集合进行区间匹配,得到对应的精确的区间,从而实现满足用户需求的模糊查询结果,最后结合实例进行演算并对结果进行分析.结果表明,该方法减少了人们设置隶属函数时的个人主观性,提高了匹配结果的准确性.
Abstract:
When people query information in a database,users often need to represent imprecise query requests. However,the traditional database can’t process the imprecise query conditions,leading to the empty query result set or numerous query results,which is difficult to filter and meet the user’s actual requirements. Many research have been proposed for dealing with fuzzy data and queries in the rela- tional database,most of them are to set different membership functions for different fuzzy sets,and applying it to large sample data will encounter many difficulties. According to fuzzy set theory and characteristics of the normal distribution function suitable for large sample data,we adopt the normal distribution density function to generalize the membership functions,which can perform interval matching for fuzzy sets automatically and obtain the corresponding exact interval of the fuzzy sets. In this way,the fuzzy query result that meets the us- er’s demand is realized,and the result is analyzed by combining with the example. It is showed that the method can reduce the individual subjectivity when setting the membership function,and improve the accuracy of matching results.

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更新日期/Last Update: 2018-09-10