METHODS OF SUPPORTING THE DECISION-MAKING ON THE POSSIBILITY OF PROVIDING GENERAL MEDICAL, THERAPEUTIC AND DENTAL SERVICES ON THE BASIS OF CIVIL LAW - SHARKADI, MARIANNA; MLAVETS, YURII (Herald of Khmelnytskyi National University. Technical sciences, 2024)
Modern information technologies open up many new opportunities in everyday life and in science. Modern information technologies are also necessary for mathematical modeling. Fuzzy logic is based on the principles of the fuzzy set introduced by Lotfi Zadeh and allows describing complex systems in the form of a set of fuzzy rules. To implement such an approach, there are specialized software tools, among which the Fuzzy Logic Toolbox occupies a special place - an addition to MATLAB that provides a wide range of tools for creating, analyzing and optimizing fuzzy models. When constructing a Mamdani-type fuzzy inference (Fuzzy Inference) system, an important step is to construct a knowledge base for the system in question. The article is devoted to the demonstration of the use of Fuzzy Logic Toolbox for mathematical modeling. The main stages of working with the application, such as building a rule base, choosing membership functions, methods of aggregation and defuzzification, are considered. The use of Fuzzy Logic Toolbox in mathematical modeling allows to significantly expand the possibilities of analysis and forecasting of systems where classical methods are not effective enough.This article is devoted to the study of the use of one of the most common mathematical software packages Matlab. The Matlab software package includes several applications, in particular, the Fuzzy Logic Designer application. The use of the Fuzzy Logic Designer program in the construction of a fuzzy mathematical model is demonstrated. The article also discusses the fuzzy inference algorithms implemented in the program, membership functions that can be used, the method of building a knowledge base, and defuzzification methods.