Hybrid adaptive decision-making model based on fuzzy logic and artificial immune systems

In modern decision support systems (DSS) characterized by processing large amounts of information in conditions of uncertainty, intellectualization of data processing and analysis is required, as well as the use of efficient technologies, methods, and decision-making models. The article suggests a decision-making model (DMM), presented in the form of rules for fuzzy inference, trained and adapted on highperformance systems using artificial immune systems. The process of learning and adaptation consists in setting the form and parameters of membership functions, as well as the parameters and structure of the base of fuzzy rules.