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Features of control processes in organizational-technical (technological) systems of continuous type

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Języki publikacji
EN
Abstrakty
EN
Technological complexes of various industries are characterized by certain modes of operation (technological regulations), which correspond to the set of variables of different nature, which have a high-dynamics of change and determine the main technical and economic performance of the object. The aim of the research is to identify information software approaches to support decision-making in organizational-technical (technological) systems. Research results are obtained through grouping, generalization and comparison methods. The scientific significance of the results are to determine the objective need to use intelligent decision support subsystems to quickly manage complex organizational-technical systems based on both: clear and formalized data and knowledge and high-quality fuzzy estimates.
Twórcy
  • ŁUKASIEWICZ Research Network – Industrial Research Institute for Automation and Measurements PIAP, Jerozolimskie 202, 02-486 Warsaw, Poland
  • National University of Food Technologies, 68 Volodymyrska Street, 01033, Kyiv, Ukraine
autor
  • – National University of Food Technologies, 68 Volodymyrska Street, 01033, Kyiv, Ukraine
  • National University of Food Technologies, 68 Volodymyrska Street, 01033, Kyiv, Ukraine
Bibliografia
  •  [1] I. Korobiichuk, A. Ladanyuk, L. Vlasenko and N. Zaiets, “Modern Development Technologies and Investigation of Food Production Technological Complex Automated Systems”. In: Proceedings of the 2nd International Conference on Mechatronics Systems and Control Engineering – ICMSCE 2018, 2018, 52–56, DOI: 10.1145/3185066.3185075.
  •  [2] I. Korobiichuk, N. Lutskaya, A. Ladanyuk, S. Naku, M. Kachniarz, M. Nowicki and R. Szewczyk, “Synthesis of Optimal Robust Regulator for Food Processing Facilities”. In: R. Szewczyk, C. Zieliński and M. Kaliczyńska (eds.), Automation 2017, vol. 550, 2017, 58–66, DOI: 10.1007/978-3-319-54042-9_5.
  •  [3] V. Tregub, I. Korobiichuk, O. Klymenko, A. Byrchenko and K. Rzeplińska-Rykała, “Neural Network Control Systems for Objects of Periodic Action with Non-linear Time Programs”. In: R. Szewczyk, C. Zieliński and M. Kaliczyńska eds.), Automation 2019, vol. 920, 2020, 155–164,DOI: 10.1007/978-3-030-13273-6_16.
  •  [4] V. Sidletskyi, I. Korobiichuk, A. Ladaniuk, I. Elperin and K. Rzeplińska-Rykała, “Development of the Structure of an Automated Control System Using Tensor Techniques for a Diffusion Station”. In: R. Szewczyk, C. Zieliński and M. Kaliczyńska (eds.), Automation 2019, vol. 920, 2020, 175–185, DOI: 10.1007/978-3-030-13273-6_18.
  •  [5] A. P. Ladanyuk, N. M. Lutska, V. D. Kishenko, L. O. Vlasenko and V. V. Ivaschuk, Methods of conventional theory of management, Lira: Kyiv, 2018 (in Ukrainian).
  •  [6] J. Stahre, L. Mårtensson (eds.), Proceedings of 8th IFAC Symposium on Automated Systems Based on Human Skill and Knowledge, Elsevier, 2003.
  •  [7] M. P. Kazmierkowski, “Integration Technologies for Industrial Automated Systems”, IEEE Industrial Electronics Magazine, vol. 1, no. 1, 2007, 51–52, DOI: 10.1109/MIE.2007.357179.
  •  [8] A. Colombo, T. Bangemann, S. Karnouskos, J. Delsing, P. Stluka, R. Harrison, F. Jammes, and J. L. Lastra (eds.), Industrial Cloud-Based CyberPhysical Systems: The IMC-AESOP Approach, Springer International Publishing, 2014, DOI: 10.1007/978-3-319-05624-1.
  •  [9] W. Q. Yan, Introduction to Intelligent Surveillance: Surveillance Data Capture, Transmission, and Analytics, Springer International Publishing, 2019, DOI: 10.1007/978-3-030-10713-0.
  • [10] T. O. Prokopenko and A. P. Ladanyuk, Information technology management organizational and technological systems, Vertikal: Cherkasi, 2015 (in Ukrainian).
  • [11] J. Yu, Y. Li, M. Chen, B. Zhang and W. Xu, “Decision-theoretic rough set in lattice-valued decision information system”, Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, 2019, 3289–3301, DOI: 10.3233/JIFS-172111.
  • [12] S. Hrybkov and H. Oliinyk, “Modeling of the decision support system structure in the planning and controlling of contracts implementation”, Ukrainian Journal of Food Science, vol. 3, no. 1, 2015, 123–130.
  • [13] T. Bakshi, B. Sarkar and S. K. Sanyal, “An Evolutionary Algorithm for Multi-criteria Resource Constrained Project Scheduling Problem based On PSO”, Procedia Technology, vol. 6, 2012, 231–238, DOI: 10.1016/j.protcy.2012.10.028.
  • [14] S. Hrybkov, H. Oliinyk and V. Litvinov, “Weboriented decision support system for planning agreements execution”, Eastern-European Journal of Enterprise Technologies, vol. 3, no. 2 (93), 2018, 13–24, DOI: 10.15587/1729-4061.2018.132604.
  • [15] N. M. Lutska and A. P. Ladanyuk, Optimal and robust control systems for technological objects, Lira: Kyiv, 2015 (in Ukrainian).
  • 16] F. Jabari, S. Nojavan, B. Mohammadi-Ivatloo, H. Ghaebi and M.-B. Bannae-Sharifian, “Robust Unit Commitment Using Information Gap Decision Theory”. In: B. Mohammadi-ivatloo and M. Nazari-Heris (eds.), Robust Optimal Planning and Operation of Electrical Energy Systems, 2019, 79–93, DOI: 10.1007/978-3-030-04296-7_5.
  • [17] H.-J. Zimmermann, “Fuzzy set theory”, WIREs omputational Statistics, vol. 2, no. 3, 2010, 317–332, DOI: 10.1002/wics.82.
  • [18] W. Pedrycz and P. Rai, “Collaborative clustering with the use of Fuzzy C-Means and its quantification”, Fuzzy Sets and Systems, vol. 159, no. 18, 2008, 2399–2427, DOI: 10.1016/j.fss.2007.12.030.
  • [19] E. Lughofer, A.-C. Zavoianu, M. Pratama and T. Radauer, “Automated Process Optimization in Manufacturing Systems Based on Static and Dynamic Prediction Models”. In: E. Lughofer and M. Sayed-Mouchaweh (eds.), Predictive Maintenance in Dynamic Systems, 2019, 485–531, DOI: 10.1007/978-3-030-05645-2_17.
  • [20] J. Beran, Mathematical Foundations of Time Series Analysis: A Concise Introduction, Springer International Publishing, 2017, DOI: 10.1007/978-3-319-74380-6.
  • [21] N. T. Son and D. T. Anh, “Discovering Time Series Motifs Based on Multidimensional Index and Early Abandoning”. In: N.-T. Nguyen, K. Hoang and P. Jȩdrzejowicz (eds.), Computational ollective Intelligence. Technologies and Applications, 2012, 72–82, DOI: 10.1007/978-3-642-34630-9_8.
  • [22] A. Tapinos and P. Mendes, “A Method for Comparing Multivariate Time Series with Different Dimensions”, PLOS ONE, vol. 8, no. 2, 2013, DOI: 10.1371/journal.pone.0054201.
  • [23] L. Wei and E. Keogh, “Semi-Supervised Time Series Classification”. In: Proceedings of the 12th ACM SIGKDD International Conference on nowledge Discovery and Data Mining, 2006, 748–753, DOI: 10.1145/1150402.1150498.
  • [24] S. Hira and P. S. Deshpande, “Data Analysis using Multidimensional Modeling, Statistical Analysis and Data Mining on Agriculture Parameters”, Procedia Computer Science, vol. 54, 2015, 431–439, DOI: 10.1016/j.procs.2015.06.050.
  • [25] J. Burgess, Wavelets: Principles, Analysis and Applications, Nova Science Publishers, Incorporated, 2018.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3503f0b0-810a-49b7-8ba3-bb46baa8781b
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