Application of Artificial Intelligence Methods and Cognitive Technologies in Dynamic Systems Modeling Problems
Abstract
The issues related to the use of artificial intelligence methods and cognitive technologies in the modeling of controlled dynamic systems are considered. An overview of the results of solving some cognitive modeling problems is presented. Aspects of the construction and research of cognitive models using various artificial intelligence tools are studied. The features of the application of neural networks, evolutionary algorithms, object-oriented programming languages, multi-agent architectures in cognitive modeling problems are characterized. The description of application fields for artificial intelligence and cognitive technologies in the problems of modeling dynamic systems is given. Methodological support for the study of the trajectory dynamics of intelligent control systems is considered. An approach to the design of dynamic cognitive maps for modeling pendulum systems using intelligent technologies is proposed. A generalized algorithm for stabilization of the pendulum using fuzzy cognitive maps is developed. The considered approach makes it possible to synthesize models of an inverted pendulum taking into account various physical effects and solve a number of problems of controlling pendulum systems. The obtained results can be used in the design and improvement of controlled technical systems.
References
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22. Borisov V.V., Luferov V.S. The method of multidimensional analysis and forecasting states of complex systems and processes based on Fuzzy Cognitive Temporal Models. Sistemy upravlenija, svjazi i bezopasnosti = Systems of Control, Communication and Security. 2020; (2):1-23. (In Eng.) doi: https://doi.org/10.24411/2410-9916-2020-10201
23. Miao Y., Liu Z.-Q Siew C.K., Miao C.Y. Dynamical cognitive network – an extension of fuzzy cognitive map. IEEE Transactions on Fuzzy Systems. 2001; 9(5):760-770. (In Eng.) doi: https://doi.org/10.1109/91.963762
24. Poczeta К., Papageorgiou E.I., Gerogiannis V.C. Fuzzy Cognitive Maps Optimization for Decision Making and Prediction. Mathematics. 2020; 8(11):2059. (In Eng.) doi: https://doi.org/10.3390/math8112059
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2. Byaletskaya E.M., Kvyatkovskaya I.Yu. O principah kognitivnogo modelirovanija slozhnyh sistem [Principles of cognitive modeling complex systems]. Vestnik Astrahanskogo gosudarstvennogo tehnicheskogo universiteta = Vestnik of Astrakhan State Technical University. 2006; (1):116-119. Available at: https://www.elibrary.ru/item.asp?id=11528197 (accessed 14.12.2021). (In Russ., abstract in Eng.)
3. Gorelova G.V., Kalinichenko A.I. Instrumentarij kognitivnogo modelirovanija slozhnyh sistem [Cognitive modeling software system]. Proceedings of the International Conference on System analysis in design and management. SPbPU, SPb.; 2018. p. 399-412. Available at: https://www.elibrary.ru/item.asp?id=35506616 (accessed 14.12.2021). (In Russ., abstract in Eng.)
4. Kalinichenko A.I. O programmnoj sisteme kognitivnogo modelirovanija slozhnyh sistem kak jelemente iskusstvennogo intellekta [On the program system of cognitive modeling of complex systems as an element of artificial intelligence]. Proceedings of the International Conference on System analysis in design and management. Polytech-Press, SPb.; 2019. p. 471-478. Available at: https://www.elibrary.ru/item.asp?id=38582570 (accessed 14.12.2021). (In Russ., abstract in Eng.)
5. Borisov V.V., Fedulov A.S. Nechetkij kognitivnyj analiz i modelirovanie slabo formalizuemyh problem [Fuzzy cognitive analysis and simulation of non-formalized problems]. Sistemy komp’yuternoj matematiki i ih prilozheniya = Computer Mathematics Systems and Their Applications. 2018; (19):113-117. Available at: https://www.elibrary.ru/item.asp?id=35177106 (accessed 14.12.2021). (In Russ., abstract in Eng.)
6. Lutsenko E.V., Serga G.V. Teorija informacii i kognitivnye tehnologii v modelirovanii slozhnyh mnogoparametricheskih dinamicheskih tehnicheskih system [The information theory and cognitive technologies in modeling complex multivariable dynamic technical systems]. Nauchnyj zhurnal KubGAU = Scientific Journal of KubGAU. 2016; (121):68-115. (In Russ., abstract in Eng.) doi: https://doi.org/10.21515/1990-4665-121-002
7. Veremey E.I. Cognitive implementation of optimization approach to the control system design for moving object. Sovremennye informacionnye tehnologii i IT-obrazovanie = Modern Information Technologies and IT-Education. 2016; 12(1):98-107. Available at: https://www.elibrary.ru/item.asp?id=27539223 (accessed 14.12.2021). (In Russ., abstract in Eng.)
8. Red'ko V.G., Sokhova Z.B. On the way to the modeling of thinking. Izvestija Kabardino-Balkarskogo nauchnogo centra RAN = News of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences. 2017; (6-2):203-209. Available at: https://www.elibrary.ru/item.asp?id=32826404 (accessed 14.12.2021). (In Russ., abstract in Eng.)
9. Kulinich A.A. Komp'juternye sistemy modelirovanija kognitivnyh kart: podhody i metody [Cognitive Maps Modelling Computer Systems: Approaches and Methods]. Problemy upravlenija = Control Sciences. 2010; (3)2-16. Available at: https://elibrary.ru/item.asp?id=14931713 (accessed 14.12.2021). (In Russ., abstract in Eng.)
10. Zagranovskaya A.V. Sistemnyj analiz na osnove nechetkih kognitivnyh kart [System analysis on the basis of imprecise cognitive cards]. Vestnik Rossijskogo jekonomicheskogo universiteta imeni G.V. Plehanova = Vestnik of the Plekhanov Russian University of Economics. 2018; (4):152-160. Available at: https://elibrary.ru/item.asp?id=35418420 (accessed 14.12.2021). (In Russ., abstract in Eng.)
11. Shcherbatov I.A. Nechetkie kognitivnye karty kak instrument predstavlenija struktur slaboformalizuemyh sistem [Fuzzy cognitive maps as a tool for structure representation of poorly formalizable systems]. Proceedings of the V International Jubilee Scientific Conference on Problems of Management, Processing and Transmission of Information. SSTU, Saratov; 2017. p. 375-378. Available at: https://elibrary.ru/item.asp?id=32690641 (accessed 14.12.2021). (In Russ., abstract in Eng.)
12. Gorelova G.V. Issledovanie problem sistemy obrazovanija. Kognitivnoe modelirovanie [Research of problems of the education system. Cognitive modeling]. Obrazovatel'nye tehnologii = Educational Technologies. 2018; (3):60-75. Available at: https://elibrary.ru/item.asp?id=35606519 (accessed 14.12.2021). (In Russ.)
13. Druzhinina O.V., Igonina E.V., Masina O.N., Petrov A.A. Aspects of Prototyping Technologies and Artificial Intelligence Use in the Framework of the Digital Transformation of the Educational Process. Sovremennye informacionnye tehnologii i IT-obrazovanie = Modern Information Technologies and IT-Education. 2020; 16(1):50-63. (In Russ., abstract in Eng.) doi: https://doi.org/10.25559/SITITO.16.202001.50-63
14. Kostenko K.I., Lebedeva A.P., Levitskii B.E. Cognitive structures analysis and synthesis for simulation the knowledge areas contents. Sovremennye informacionnye tehnologii i IT-obrazovanie = Modern Information Technologies and IT-Education. 2016; 12(2):50-55. Available at: https://elibrary.ru/item.asp?id=28151020 (accessed 14.12.2021). (In Russ., abstract in Eng.)
15. Vassilyev S.N. Ot klassicheskih zadach regulirovanija k intellektual'nomu upravleniju. I [From classical control problems to intelligent control]. Intellektual'nye Sistemy = Intelligent Systems. 1999; 4(1-2):19-72. Available at: https://elibrary.ru/item.asp?id=38546791 (accessed 14.12.2021). (In Russ.)
16. Vassilyev S.N. Ot klassicheskih zadach regulirovanija k intellektnomu upravleniju. II [From classical problems of regulation to intellektny management]. Intellektual'nye Sistemy = Intelligent Systems. 1999; 4(3-4):5-48. Available at: https://elibrary.ru/item.asp?id=37341654 (accessed 14.12.2021). (In Russ.)
17. Vassilyev S.N., Novikov D.A., Bakhtadze N.N. Intelligent Control of Industrial Processes. IFAC Proceedings Volumes. 2013; 46(9):49-57. (In Eng.) doi: https://doi.org/10.3182/20130619-3-RU-3018.00643
18. Druzhinina O.V., Korepanov E.R., Belousov V.V., Masina O.N., Petrov A.A. Development of the instrumental support of the domestic computing platform "Elbrus 801-PC" in the problems of neural network modeling of nonlinear dynamic systems. Nelinejnyj mir = Nonlinear World. 2021; 19(1):15-28. (In Russ., abstract in Eng.) doi: https://doi.org/10.18127/j20700970-202101-02
19. Druzhinina O.V., Masina O.N., Petrov A.A. Up-to-date Software and Methodological Support for Studying Models of Controlled Dynamic Systems Using Artificial Intelligence. In: Silhavy R. (ed.) Informatics and Cybernetics in Intelligent Systems. CSOC 2021. Lecture Notes in Networks and Systems. Vol. 228. Springer, Cham; 2021. p. 670-681. (In Eng.) doi: https://doi.org/10.1007/978-3-030-77448-6_65
20. Fedulov A.S., Borisov V.V. Models of System Dynamics Based on Fuzzy Relational Cognitive Maps. Sistemy upravlenija, svjazi i bezopasnosti = Systems of Control, Communication and Security. 2016; (1):66-80. Available at: https://elibrary.ru/item.asp?id=25624932 (accessed 14.12.2021). (In Russ., abstract in Eng.)
21. Chi Y., Liu J. Learning of Fuzzy Cognitive Maps With Varying Densities Using A Multiobjective Evolutionary Algorithm. IEEE Transactions on Fuzzy Systems. 2016; 24(1):71-81. (In Eng.) doi: https://doi.org/10.1109/TFUZZ.2015.2426314
22. Borisov V.V., Luferov V.S. The method of multidimensional analysis and forecasting states of complex systems and processes based on Fuzzy Cognitive Temporal Models. Sistemy upravlenija, svjazi i bezopasnosti = Systems of Control, Communication and Security. 2020; (2):1-23. (In Eng.) doi: https://doi.org/10.24411/2410-9916-2020-10201
23. Miao Y., Liu Z.-Q Siew C.K., Miao C.Y. Dynamical cognitive network – an extension of fuzzy cognitive map. IEEE Transactions on Fuzzy Systems. 2001; 9(5):760-770. (In Eng.) doi: https://doi.org/10.1109/91.963762
24. Poczeta К., Papageorgiou E.I., Gerogiannis V.C. Fuzzy Cognitive Maps Optimization for Decision Making and Prediction. Mathematics. 2020; 8(11):2059. (In Eng.) doi: https://doi.org/10.3390/math8112059
25. Kim J.-Y., Cho S.-B. Electric energy consumption prediction by deep learning with state explainable autoencoder. Energies. 2019; 12(4):739. (In Eng.) doi: https://doi.org/10.3390/en12040739
26. Orang O., Silva R., de Lima e Silva P.C., Guimaraes F.G. Solar Energy Forecasting With Fuzzy Time Series Using High-Order Fuzzy Cognitive Maps. 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE Press; 2020. p. 1-8. (In Eng.) doi: https://doi.org/10.1109/FUZZ48607.2020.9177767
Published
2022-03-31
How to Cite
DRUZHININA, Olga Valentinovna; MASINA, Olga Nikolaevna; IGONINA, Elena Viсtorovna.
Application of Artificial Intelligence Methods and Cognitive Technologies in Dynamic Systems Modeling Problems.
Modern Information Technologies and IT-Education, [S.l.], v. 18, n. 1, p. 83-97, mar. 2022.
ISSN 2411-1473. Available at: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/824>. Date accessed: 03 aug. 2025.
doi: https://doi.org/10.25559/SITITO.18.202201.83-97.
Section
Cognitive information technologies in control systems
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