HIERARCHICAL FACTOR CLASSIFICATION ANALYSIS IN THE FRAMEWORK OF INFORMATIONEXTREME INTELLECTUAL TECHNOLOGY
DOI:
https://doi.org/10.20998/2079-0023.2025.01.09Keywords:
automated control systems, chemical-technological processes, mathematical models, hierarchical factor classification analysis, information-extreme intelligent technologiesAbstract
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.
References
Ridkokasha A. A., Holder K. K. Osnovy system shtuchnoho intelektu: Navchalnyi posibnyk. Cherkasy: Vidlunnia-Plius Publ., 2002. 240 p. (In Ukr.).
Dovbysh A. S. Osnovy proektuvannia intelektualnykh system: Navchalnyi posibnyk. Sumy: Vydavnytstvo SumDU Publ., 2009. 172 p. (In Ukr.).
Shelehov I., Prylepa D., Khibovska Y. Information-Extreme Machine Learning of an Ophthalmic Diagnostic System with a Hierarchical Class Structure. Artificial Intelligence. 2024, no. 3, pp. 114–125. DOI: https://doi.org/10.15407/jai2024.03.114.
Zhang H., Han J. Mathematical models for information classification and recognition of multi-target optical remote sensing images. Open Physics. 2020. Vol. 18, issue 1, id.123, 10 p. DOI: 10.1515/phys-2020-0123.
Park S., Zhang Y., Yu S. X., Beery S., Huang J. Visually Consistent Hierarchical Image Classification. ICLR. 2025. URL: Available at: https://openreview.net/forum?id=7HEMpBTb3R.
Bloor M., Ahmed A., Kotecha N., Mercangöz M., Tsay C., del Río-Chanona E. A.. Control-Informed Reinforcement Learning for Chemical Processes. Industrial & Engineering Chemistry Research. 2025, no. 64 (9), pp. 4966–4978. DOI: https://doi.org/10.1021/acs.iecr.4c03233.
Syed M. J., Hashmani M., Rehman M., Budiman A. An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment. Sensors. 2020, vol. 20, issue 20, article 5811. DOI: https://doi.org/10.3390/s20205811.
Shelehov I. V., Barchenko N. L., Prylepa D. V. Information-extreme machine training system of functional diagnosis system with hierarchical data structure. Radio Electronics, Computer Science, Control, 2022, vol. 2, pp. 189–200. DOI: https://doi.org/10.15588/1607-3274-2022-18.
Moskalenko V. V., Moskalenko A. S., Korobov A. G. Models and methods of intellectual information technology of autonomous navigation for compact drones. Radio Electronics, Computer Science, Control. 2018, vol. 3. DOI: https://doi.org/10.15588/1607-3274-2018-3-8.
Bernabei M., Costantino F. Adaptive automation: Status of research and future challenges. Robotics and Computer-Integrated Manufacturing. 2024, vol. 88, issue 3, article 102724. DOI: 10.1016/j.rcim.2024.102724.
Javaid M., Haleem A., Singh R. P., Suman R. Enabling flexible manufacturing system (FMS) through the applications of industry 4.0 technologies. Internet of Things and Cyber-Physical Systems. 2022, vol. 2, pp. 49–62. DOI: 10.1016/j.iotcps.2022.05.005.
Prylepa D. V. Informatsiino-ekstremalna intelektualna tekhnolohiia diahnostuvannia emotsiino-psykhichnoho stanu liudyny : dys. … kand. tekhn. nauk : 05.13.06. Kharkiv, 2024. 188 p. (In Ukr.).
Dovbysh A., Zimovets V. Hierarchical Algorithm of the Machine Learning for the System of Functional Diagnostics of the Electric Drive. Advanced Information Systems and Technologies: Proceedings of the VI International Scientific Conference. 2018. pp. 85–88.
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