一种用于生成语言变量的数值项的启发式算法

IF 0.5 Q4 BUSINESS
E. Chujkova, V. V. Galushka
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引用次数: 0

摘要

本文描述了一种易于实现的语言变量词隶属函数的自动生成算法,该算法允许在基于定性标准的关系数据库中使用SQL查询语言进行信息搜索。该算法可以在考虑数据库中感兴趣变量分布的情况下计算三角形和梯形隶属函数的参数。该算法定义术语库所覆盖的区间,以便每个区间包含大约相同数量的值。利用所定义区间的上界计算隶属函数的参数。利用SQL语言有限的计算手段,可以很容易地计算出用该算法生成的隶属函数的参数。我们回顾了基于包含100或500个不同值的数据库样本生成语言变量的3和5项的算法实现。通过该算法得到的隶属函数具有有序性、完备性、一致性和正态性。它们不需要进一步的近似。与已知的方法不同,该算法不需要大量的计算资源,使用专门的软件,设置配置或训练集形成。算法实现创造了使用SQL语言在关系数据库中支持模糊搜索查询的机会,尽管这些查询是有限的。从而提高系统的智能水平,并为用户提供以自然语言表达搜索查询的手段。该算法生成的语言变量项可以在基于模糊规则的信息系统知识库框架内使用,也可以进行模糊推理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A heuristic algorithm for generating the numerical terms of a linguistic variable
In this paper we describe an easy-to-implement algorithm for automatedgeneration of the linguistic variable term membership functions to allow for information search in a relational database based on qualitative criteria by means of the SQL query language. The proposed algorithm makes it possible to calculate the parameters of the triangular and trapezoid membership functions taking into account the distribution of the variable of interest stored in the database. The algorithm defines the intervals covered by the term bases, so that each interval contains about the same number of values. Upper bounds of the defined intervals are used to calculate the parameters of membership functions. The parameters of the membership functions generated with this algorithm can be easily calculated with the limited computational means of the SQL language. We review the algorithm realizations for the generation of 3 and 5 terms of a linguistic variable based on a sample from a database containing 100 or 500 different values. The membership functions obtained through the algorithm have the required properties of orderliness, completeness, consistency and normality. They do not require further approximation. Unlike the known methods, the algorithm does not require significant computing resources, the use of specialized software, settings configuring, or a training set formation. The algorithm implementation creates opportunities to support fuzzy search queries in relational databases using the means of the SQL language, as limited as they are. Thus, the system’s level of intelligence would be increased, and the user would be provided with the means of search query formulation in a natural language. The linguistic variable terms generated using our algorithm can be used within the framework of a fuzzy rule-based knowledge base of an information system, as well as to perform fuzzy inference.
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