Voir la notice de l'article provenant de la source Math-Net.Ru
@article{PFMT_2024_1_a13, author = {V. S. Smorodin and V. A. Prohorenko}, title = {Intellectual adaptive control system with feedback connections}, journal = {Problemy fiziki, matematiki i tehniki}, pages = {93--98}, publisher = {mathdoc}, number = {1}, year = {2024}, language = {ru}, url = {http://geodesic.mathdoc.fr/item/PFMT_2024_1_a13/} }
V. S. Smorodin; V. A. Prohorenko. Intellectual adaptive control system with feedback connections. Problemy fiziki, matematiki i tehniki, no. 1 (2024), pp. 93-98. http://geodesic.mathdoc.fr/item/PFMT_2024_1_a13/
[1] V.S. Smorodin, I.V. Maksimei, Metody i sredstva imitatsionnogo modelirovaniya tekhnologicheskikh protsessov proizvodstva, monografiya, M-vo obrazovaniya RB, Gomelskii gosudarstvennyi universitet imeni Frantsiska Skoriny, GGU im. F. Skoriny, Gomel, 2007, 369 pp.
[2] V.A. Prokhorenko i dr., “Avtomatizatsiya proizvodstvennoi deyatelnosti v ramkakh Ekosistemy OSTIS”, Tekhnologiya kompleksnoi podderzhki zhiznennogo tsikla semanticheski sovmestimykh intellektualnykh kompyuternykh sistem novogo pokoleniya, monografiya, Gl. 7.7, ed. V.V. Golenkov, BGUIR, Minsk, 2023, 805–830
[3] V. Smorodin, V. Prokhorenko, “Application of Neuro-Controller Models for Adaptive Control”, Recent Developments in Data Science and Intelligent Analysis of Information. ICDSIAI 2018, Advances in Intelligent Systems and Computing, 836, no. 7, ed. O.Chertov et al., Springer, Cham, 2018, 30–38 | DOI
[4] V. Smorodin, V. Prokhorenko, “Software-Technological Complex For Adaptive Control Of A Production Cycle Of Robotic Manufacturing”, Open semantic technologies for intelligent systems, 2022, no. 6, 401–404
[5] D. Ivaniuk, V. Taberko, V. Smorodin, V. Prokhorenko, “Adaptive Control System for Technological Process within OSTIS Ecosystem”, Open semantic technologies for intelligent systems, 2023, no. 7, 291–298
[6] V. Smorodin, V. Prokhorenko, “Control Of A Technological Cycle Of Production Process Based On A Neuro-Controller Model”, Open semantic technologies for intelligent systems, 2019, no. 3, 251–256
[7] P. Ladosz, L. Weng, M. Kim, H. Oh, “Exploration in deep reinforcement learning: A survey”, Information Fusion, 85 (2022), 1–22 | DOI | MR
[8] Yu.V. Nikityuk, V.A. Prokhorenko, A.I. Kulyba, “Mnogokriterialnaya optimizatsiya parametrov lazernoi rezki kvartsevogo stekla s primeneniem neirosetevogo modelirovaniya i geneticheskogo algoritma”, Problemy fiziki, matematiki i tekhniki, 2023, no. 3 (56), 26–31
[9] K.O. Stanley, R. Miikkulainen, “Evolving Neural Networks Through Augmenting Topologies”, Evolutionary Computation, 10:2 (2002), 99–127 | DOI