An Approach to Generating Fuzzy Rules for a Fuzzy Controller Based on the Decision Tree Interpretation

This article describes solutions to control problems using fuzzy logic, which facilitates the development of decision support systems across various fields. However, addressing this task through the manual creation of rules in specific fields necessitates significant expert knowledge. Machine learning methods can identify hidden patterns. A key novelty of this approach is the algorithm for generating fuzzy rules for a fuzzy controller, derived from interpreting a decision tree. The proposed algorithm allows the quality of the control actions in organizational and technical systems to be enhanced. This article presents an example of generating a set of fuzzy rules through the analysis of a decision tree model. The proposed algorithm allows for the creation of a set of fuzzy rules for constructing fuzzy rule-based systems (FRBSs). Additionally, it autogenerates membership functions and linguistic term labels for all of the input and output parameters. The machine learning model and the FRBS obtained were assessed using the coefficient of determination (R2). The experimental results demonstrated that the constructed FRBS performed on average 2% worse than the original decision tree model. While the quality of the FRBS could be enhanced by optimizing the membership functions, this topic falls outside the scope of the current article.