The purpose of the research work is to develop an algorithm for interpreting input variables by a discrete membership function of a fuzzy set for embedded and real-time systems. To achieve the goal set in the scientific work, the following tasks were solved: the study of fuzzy logical inference in automatic control systems: analysis of the fuzzy set building, representation of a discrete input values; development of an algorithm and software implementation for real-time systems. Methods for setting input variables for fuzzy logic systems were considered, an algorithmic implementation of the representation of the input value as an element of a fuzzy set and automatic calculation of the membership function was proposed.

The paper considers an educational information system built based on gamification and game technologies, expanding the possibilities of supporting the educational process. A classification was proposed, and the main features of information training systems were presented. An educational information system model was developed to support the educational process based on the gamification method. It was shown that many factors included in the education information system contribute to the enhancement of efficiency of such systems. A comparison of the gamification method with other gaming techniques was given. A training simulator was developed to consolidate theoretical knowledge and form practical skills and abilities for trainees in the oil and gas equipment and electric power engineering

The purpose of the research work is to optimize the architecture of fuzzy automated systems based on the state monitoring algorithm. To achieve the goal set in the scientific work, the following problems were solved: the study of a simulation description based on the discount state of authorized systems; development of an architectural solution based on the state monitoring algorithm. An algorithmic implementation of the state observer was proposed. The results showed that the proposed system allows expanding the functionality of the automated systems and increasing their performance

The structure of production at the field, the structure of data obtained, as well as the features of their interpretation and modeling, were investigated. A mathematical model was developed for predicting oil production and transportation processes under conditions of uncertainty based on the results of an analysis of production equipment. The existing data model for individual oil field facilities was analyzed. An algorithm for the operation of a neural network model was proposed to predict an important characteristic of an objectprofitability. The algorithm was improved based on the optimization block, which served to classify and identify features in existing data based on the p-criterion. The proposed algorithms are designed to make decisions when performing various types of operations in the field. The proposed model for predicting oil production and transportation processes under conditions of uncertainty showed the efficiency of profitability projection at the level of 73.5%.