Fuzzy classification knowledge base construction based on trend rules and inverse inference

Authors

DOI:

https://doi.org/10.15587/1729-4061.2015.36934

Keywords:

fuzzy relations, inverse logical inference, solving systems of fuzzy logical equations

Abstract

In this paper, an approach to fuzzy classification rules construction within the framework of fuzzy relation equations is proposed. At the same time, the system of fuzzy trend rules serves as a carrier of expert information and generator of rules - solutions of fuzzy relation equations. The system of fuzzy classification rules can be rearranged as a set of linguistic solutions of fuzzy relation equations using the composite system of fuzzy terms, e.g. “significant rise”, “essential drop” etc., where causes and effects significance measures are described by fuzzy quantifiers. The problem of inverse logical inference, which lies in restoring the coordinates of the maximum of the fuzzy input terms membership functions for each output class is reduced to solving the system of fuzzy relation equations using a genetic algorithm.

The proposed approach allows to avoid the alternative rule selection. The aim of the rule selection methods is to reduce the system complexity by removing inefficient and redundant rules and improve the system accuracy by introducing alternative rules into the final rule base. Using expert knowledge cannot guarantee the optimal cooperation activity among rules. The rule selection problem is still relevant since there is currently no single methodical standard for the optimal structural adjustment of fuzzy classification knowledge bases.

Solving fuzzy relation equations using the genetic algorithm ensures the optimal number of fuzzy rules for each output term and optimal form of the membership functions of the fuzzy input terms for each linguistic solution.

Consecutive solution of the optimization problems provides complexity reduction of the problem of fuzzy classification knowledge bases generation. 

Author Biography

Ганна Борисівна Ракитянська, Vinnitsa National Technical University Khmelnitske Sh. 95, Vinnitsa, Ukraine, 21021

Associate professor, Candidate of technical science

The department of software design

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Published

2015-02-27

How to Cite

Ракитянська, Г. Б. (2015). Fuzzy classification knowledge base construction based on trend rules and inverse inference. Eastern-European Journal of Enterprise Technologies, 1(3(73), 25–32. https://doi.org/10.15587/1729-4061.2015.36934

Issue

Vol. 1 No. 3(73) (2015): Control processes

Section

Control processes

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Copyright (c) 2015 Ганна Борисівна Ракитянська

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