Науковий вісник НГУ

The synthesis of strategies for the efficient performance of sophisticated technological complexes based on the cognitive simulation modelling

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №2 2021

Last Updated on 29 April 2021

Published on 30 November -0001

Hits: 3281

Authors:

N.A.Zaiets, orcid.org/0000-0001-5219-2081, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O.V.Savchuk, orcid.org/0000-0003-2519-4342, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.M.Shtepa, orcid.org/0000-0002-2796-3144, Polessky State University, Pinsk, the Republic of Belarus, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

N.M.Lutska, orcid.org/0000-0001-8593-0431, National University of Food Technologies, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

L.O.Vlasenko, orcid.org/0000-0002-2003-6313, National University of Food Technologies, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2021, (2): 110 - 117

https://doi.org/10.33271/nvngu/2021-2/110

Abstract:

Purpose. Improving the productivity and energy efficiency of complex technological complexes through the development and use of scenario-cognitive modeling in control systems.

Methodology. Fuzzy cognitive maps, in the form of a weighted oriented graph, were used to develop a scenario-cognitive model. As a result of the conducted research studies, a new strategy of generalization of an expert estimation of mutual influences of concepts on the basis of methods of the cluster analysis is offered.

Findings. Based on experimental research and object-oriented analysis of a complex technological complex, a structure of a fuzzy cognitive model is created. A scenario-cognitive model in the form of a weighted oriented graph (fuzzy cognitive map) has been developed, which illustrates a set of connections and the nature of the interaction of expertly determined factors. To solve the problem of impossibility of operative interrogation of experts in case of change in parameters of functioning of difficult technological complexes, expert estimations of values of weight coefficients of mutual influence of concepts are received. Cluster analysis methods were used to group expert assessments and determine a single value as a result of the research. The results of the scenario-cognitive modeling of the enterprise showed that production shutdowns and abnormal situations related to the failure of electrical equipment, deviations of the technological regime and the quality of wastewater treatment have a significant impact on the dynamics of productivity, energy efficiency and efficient use of equipment.

Originality. The new scenario-cognitive model developed for forecasting the situation in the absence of accurate quantitative information consists in creating a fuzzy cognitive map, for modeling which many parameters of complex technological complexes are expertly determined. Using the developed methodology, a degree of interaction of these parameters is found, which allows determining dynamics of change in target criteria of functioning under various management strategies.

Practical value. On the basis of the created scenario-cognitive model, software has been developed which allowed analyzing dynamics of change in productivity, energy efficiency and efficiency of use of the equipment under possible scenarios of functioning of difficult technological complexes is developed.

Keywords: cognitive modeling, structural analysis, forecasting, fuzzy cognitive map, cluster analysis, expert assessments

References.

1. Korobiichuk, I., Ladanyuk, A., Vlasenko, L., & Zaiets, N. (2018). Modern Development Technologies and Investigation of Food Production Technological Complex Automated Systems. Proceedings of 2^nd International Conference on Mechatronics Systems and Control Engineering ICMSCE, 52-56. https://doi.org/10.1145/3185066.3185075.

2. Zaiets, N., Vlasenko, L., Lutskaya, N., & Usenko, S. (2019). System Modeling for Construction of the Diagnostic Subsystem of the Integrated Automated Control System for the Technological Complex of Food Industries. Proceedings 3^rd International Conference on Mechatronics Systems and Control Engineering (ICMSCE), Nice, France. https://doi.org/10.1145/3314493.3314523.

3. Dudnyk, V., Sinenko, Y., Matsyk, M., Demchenko, Y., Zhyvotovskyi, R., Repilo, I., , & Shyshatskyi, A. (2020). Development of a method for training artificial neural networks for intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 3(2(105)), 37-47. https://doi.org/10.15587/1729-4061.2020.203301.

4. Olsen, R.L., Madsen, J.T., Rasmussen, J.G., & Schwefel,H.-P. (2017). On the use of information quality in stochastic networked control systems. Computer Networks, 124, 157-169.

5. Noh, B., Son, J., Park, H., & Chang, S. (2017). In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining. Sustainability, 9(11), 2119. https://doi.org/10.3390/su9112119.

6. Katranzhy, L., Podskrebko, O., & Krasko, V. (2018). Modelling the dynamics of the adequacy of banks regulatory capital. Baltic Journal of Economic Studies, 4(1), 188-194. https://doi.org/10.30525/2256-0742/2018-4-1-188-194.

7. Gerami Seresht, N., & Fayek, A.R. (2020). Neuro-fuzzy system dynamics technique for modeling construction systems. Applied Soft Computing, 93, 106400. https://doi.org/10.1016/j.asoc.2020.106400.

8. James, M., Keller, Derong Liu, & David, B. Fogel (2016). Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation. New Jersey: Wiley-IEEE Press.

9. Pievtsov, H., Turinskyi, O., Zhyvotovskyi, R., Sova, O., Zvieriev, O., Lanetskii, B., & Shyshatskyi, A. (2020). Development of an advanced method of finding solutions for neuro-fuzzy expert systems of analysis of the radioelectronic situation. EUREKA: Physics and Engineering, 4, 78-89. https://doi.org/10.21303/2461-4262.2020.001353.

10. Hassanzad, M., Orooji, A., Valinejadi, A., & Velayati, A. (2017). A fuzzy rule-based expert system for diagnosing cystic fibrosis. Electronic Physician, 9(12), 5974-5984. https://doi.org/10.19082/5974.

11. Jumani, T., Mustafa, M., Md. Rasid, M., Anjum, W., & Ayub, S. (2019). Salp Swarm Optimization Algorithm-Based Controller for Dynamic Response and Power Quality Enhancement of an Islanded Microgrid. Processes, 7(11), 840. https://doi.org/10.3390/pr7110840.

12. Kuchuk, N., Mohammed, A. S., Shyshatskyi, A., & Nalapko, O. (2019). The method of improving the efficiency of routes selection in networks of connection with the possibility of self-organization. International Journal of Advanced Trends in Computer Science and Engineering, 8(1.2), 1-6.

13. Massel, L.V., Gerget, O.M., Massel, A.G., & Mamedov,T.G. (2019). The Use of Machine Learning in Situational Management in Relation to the Tasks of the Power Industry. EPJ Web of Conferences, 217, 01010. https://doi.org/10.1051/epjconf/201921701010.

14. Govorov, P.P., Budanov, P.F., & Brovko, K.Yu. (2017). Identification Of Emergency Regimes Of Power Equipment Based On The Application Of Dynamic Fractal-Cluster Model. International Scientific Conference UNITECH 2017 Gabrovo: Proceedings. Gabrovo, 1, 57-58. https://doi.org/10.15587/1729-4061.2018.126427.

15. Weinberger, G., & Moshfegh, B. (2018). Investigating influential techno-economic factors for combined heat and power production using optimization and metamodeling. Applied Energy, Elsevier, 232, 555-571. https://doi.org/10.1016/j.apenergy.2018.09.206.

16. Budanov, P., Brovko, K., Cherniuk, A., Vasyuchenko, P., & Khomenko, V. (2018). Improving the reliability of information-control systems at power generation facilities based on the fractal-cluster theory. Eastern-European Journal of Enterprise Technologies, 2(9(92)), 4-12. https://doi.org/10.15587/1729-4061.2018.126427.

17. Raskin, L., Sira, O., & Ivanchykhin, Y. (2017). Models and methods of regression analysis under conditions of fuzzy initial data. Eastern-European Journal of Enterprise Technologies, 4(4(88)), 12-19. https://doi.org/10.15587/1729-4061.2017.107536.

18. Alford, S., Robinett, R., Milechin, L., & Kepner, J. (2019). Training Behavior of Sparse Neural Network Topologies. 2019 IEEE High Performance Extreme Computing Conference (HPEC). https://doi.org/10.1109/hpec.2019.8916385.

19. Abaci, K., & Yamacli, V. (2019). Hybrid Artificial Neural Network by Using Differential Search Algorithm for Solving Power Flow Problem. Advances in Electrical and Computer Engineering, 19(4), 57-64. https://doi.org/10.4316/aece.2019.04007.

20. Milov, O., Voitko, A., Husarova, I., Domaskin, O., Ivanchenko, Y., Ivanchenko, I., ..., & Fraze-Frazenko, O. (2019). Development of methodology for modeling the interaction of antagonistic agents in cybersecurity systems. Eastern-European Journal of Enterprise Technologies, 2(9(98)), 56-66. https://doi.org/10.15587/1729-4061.2019.164730.

• < Prev
• Next >