ІЄРАРХІЧНИЙ ФАКТОРНИЙ КЛАСИФІКАЦІЙНИЙ АНАЛІЗ В РАМКАХ ІНФОРМАЦІЙНО- ЕКСТРЕМАЛЬНОЇ ІНТЕЛЕКТУАЛЬНОЇ ТЕХНОЛОГІЇ

The application of information-extreme intelligent technologies for the management of chemical-technological processes is an important direction in the development of automation in industry, particularly in chemical enterprises. A method for designing hierarchical control systems has been proposed, based on the use of hierarchical factor classification analysis to optimize the training and self-learning of automated process control systems. The main feature of the method is the use of mathematical models to analyze the functional states of processes, which allows for the accurate determination of optimal control parameters and the adaptation of the system to real-time changes. The work demonstrates that the use of hierarchical factor classification analysis increases the effectiveness of detecting functional deviations in technological processes, reducing the likelihood of errors in decision-making. To enhance the accuracy and probability of correctly determining the states, it is proposed to use algorithms for optimizing geometric parameters and control tolerances. It has been established that this method works effectively even in complex conditions where the number of functional states may vary. The research shows that the application of hierarchical factor classification analysis is effective for optimizing management processes, providing increased decision-making reliability and stability to changes in the conditions of complex chemical-technological process production. Furthermore, the proposed approach enhances the system's ability to self-learn and adapt, making it an effective tool for future intelligent automated systems.