Visual Programming of the Area Ratio Method

Author:

Bobyr M. V.1ORCID,Khrapova N. I.1ORCID

Affiliation:

1. Southwest State University

Abstract

Purpose of research. Investigation of the relationship between the input and output characteristics of a fuzzy logic system based on the application of the area ratio method. The description of the specified method and the results obtained during modeling in a tabular processor is carried out using the means of illustrative presentation of information – visual programming.Methods. To study the area ratio method, we considered a fuzzy logic model containing two input variables with three triangular membership functions and one output variable with five triangular membership functions. A database of fuzzy rules has been formed. The degrees of activation of the output terms were determined using the minimax rule of output L. Zadeh. The defuzzification of the values was carried out using a model based on the area ratio method.Results. There are advantages of the area ratio method over traditional models, which consist in the ability to compensate for the main disadvantage - narrowing the defazzification interval. Using the proposed method, the possibility of using different numbers of variables on the input and output membership functions is studied. The results of experimental studies have shown that combining the parameters allows us to create a visual representation of the characteristics between the input and output variables.Conclusion. This article describes the area ratio method, which allows us to visualize the relationship between input and output variables. There are the main results of numerical modeling reflecting the specifics of the method. The study was conducted through visual programming, which provides a number of advantages, such as improving the quality of the software product, ensuring a clear structuring of the task and accessibility to human perception.

Publisher

Southwest State University

Reference20 articles.

1. Bobyr M. V., Emelyanov S. G. A nonlinear method of learning neuro-fuzzy models for dynamic control systems. Applied Soft Computing, 2020, vol. 88, 106030 p.

2. Bobyr M.V., Khrapova N.I., Lamonov M.A. Smart Traffic Light Control System Based on Fuzzy Logic. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta = Proceedings of the Southwest State University. 2021;25(4): 162-174. (In Russ.). https://doi.org/10.21869/2223-1560-2021-25-4-162-176

3. Bobyr M. V., Yakushev A. S., Dorodnykh A. A. Fuzzy devices for cooling the cutting tool of the CNC machine implemented on FPGA. Measurement, 2020, vol. 152, p. 107378.

4. Adriyendi. Fuzzy Logic using Tsukamoto Model and Sugeno Model on Prediction Cost. International Journal of Intelligent Systems and Applications, 2018, vol. 10, no. 6, pp. 13-21.

5. Galkin V. A., Krasilnikov S. N., Popenkov V. B., Gonzalez-Gusev H. K. Sravnenie algoritmov MAMDANI i SUGENO v zadache protsessa obucheniya ANFIS dlya otsenki QOE dostupa k internet-uslugam na baze paketa MATLAB [Comparison of MAMDANI and SUGENO algorithms in the task of the ANFIS learning process for evaluating QOE access to Internet services based on the MATLAB package]. Dinamika slozhnykh sistem - XXI vek = Dynamics of complex systems - XXI century, 2019, vol. 13, no. 2, pp. 28-33.

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