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A Comparison of Methods to Construct an Optimal Membership Function in a Fuzzy Database System

机译:模糊数据库系统中构造最优隶属度函数的方法比较

摘要

A fuzzy set is one in which membership in a category is not Boolean, rather items have a degree of membership. Fuzzy databases expand on this idea by storing fuzzy data and allowing data to be retrieved based on its degree of membership. Determining the degree of membership that satisfies the largest number of users is difficult. Five different methods of determining the membership function: the Direct Rating Method, the Random Method with step sizes of .02 and .03, the Steplock Method, and the Weighted Average Method, were compared on the basis of convergence and user satisfaction. The results support use of the Direct Rating Method and the Steplock Method in conjunction with each other, to produce the membership function in the least time and with the highest user satisfaction.
机译:模糊集是其中类别的隶属度不是布尔值,而是项目具有一定程度的隶属度的模糊集。模糊数据库通过存储模糊数据并允许根据其隶属程度来检索数据来扩展此思想。确定满足最大数量用户的成员资格程度是困难的。在收敛性和用户满意度的基础上,比较了确定隶属函数的五种不同方法:直接评分法,步长为.02和.03的随机法,步锁法和加权平均法。结果支持将Direct Rating方法和Steplock方法相互结合使用,以在最短的时间内以最高的用户满意度产生隶属函数。

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  • 作者

    Cunningham Joanne Marie;

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  • 年度 2006
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