Development of object state estimation method in intelligent decision support systems
DOI:
https://doi.org/10.15587/1729-4061.2021.239854Keywords:
decision support systems, artificial neural networks, genetic algorithmAbstract
A method of object state estimation in intelligent decision support systems (DSS) has been developed. The essence of the method is to ensure a high-quality analysis of the current state of the analyzed object. The key difference of the developed method is the use of an advanced genetic algorithm. The advanced genetic algorithm is used when constructing a fuzzy cognitive model and increases the efficiency of identifying factors and relationships between them by simultaneously finding a solution by several individuals. The objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The method also contains an improved procedure for processing initial data under a priori uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The method increases the efficiency of data processing at the level of 11–15 % using additional advanced procedures. The proposed method can be used in DSS of automated control systems (artillery units, special-purpose geographic information systems). It can also be used in DSS for aviation and air defense ACS, as well as in DSS for logistics ACS of the Armed Forces
References
- Bashkyrov, O. M., Kostyna, O. M., Shyshatskyi, A. V. (2015). Rozvytok intehrovanykh system zviazku ta peredachi danykh dlia potreb Zbroinykh Ozbroiennia ta viyskova tekhnika, 1 (5), 35–39.
- Dudnyk, V., Sinenko, Y., Matsyk, M., Demchenko, Y., Zhyvotovskyi, R., Repilo, I. et. al. (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. doi: https://doi.org/10.15587/1729-4061.2020.203301
- Maistrenko, O., Khoma, V., Karavanov, O., Stetsiv, S., Shcherba, A. (2021). Devising a procedure for justifying the choice of reconnaissance-firing systems. Eastern-European Journal of Enterprise Technologies, 1 (3 (109)), 60–71. doi: https://doi.org/10.15587/1729-4061.2021.224324
- 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. doi: https://doi.org/10.21303/2461-4262.2020.001353
- Zuiev, P., Zhyvotovskyi, R., Zvieriev, O., Hatsenko, S., Kuprii, V., Nakonechnyi, O. et. al. (2020). Development of complex methodology of processing heterogeneous data in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 4 (9 (106)), 14–23. doi: https://doi.org/10.15587/1729-4061.2020.208554
- Shyshatskyi, A., Zvieriev, O., Salnikova, O., Demchenko, Ye., Trotsko, O., Neroznak, Ye. (2020). Complex Methods of Processing Different Data in Intellectual Systems for Decision Support System. International Journal of Advanced Trends in Computer Science and Engineering, 9 (4), 5583‒5590 doi: https://doi.org/10.30534/ijatcse/2020/206942020
- Yeromina, N., Kurban, V., Mykus, S., Peredrii, O., Voloshchenko, O., Kosenko, V. et. al. (2021). The Creation of the Database for Mobile Robots Navigation under the Conditions of Flexible Change of Flight Assignment. International Journal of Emerging Technology and Advanced Engineering, 11 (05), 37‒44. doi: https://doi.org/10.46338/ijetae0521_05
- Petrosian, R., Chukhov, V., Petrosian, A. (2021). Development of a method for synthesis the FIR filters with a cascade structure based on genetic algorithm. Technology Audit and Production Reserves, 4 (2 (60)), 6–11. doi: https://doi.org/10.15587/2706-5448.2021.237271
- Alpeeva, E. A., Volkova, I. I. (2019). The use of fuzzy cognitive maps in the development of an experimental model of automation of production accounting of material flows. Russian Journal of Industrial Economics, 12 (1), 97–106. doi: https://doi.org/10.17073/2072-1633-2019-1-97-106
- Zagranovskaya, A. V., Eissner, Y. N. (2017). Simulation scenarios of the economic situation based on fuzzy cognitive maps. Modern economics: problems and solutions, 10 (94), 33‒47. doi: https://doi.org/10.17308/meps.2017.10/1754
- Simankov, V. S., Putyato, M. M. (2013). Issledovanie metodov kognitivnogo analiza. Sistemniy analiz, upravlenie i obrabotka informatsii, 13, 31‒35.
- Ko, Y.-C., Fujita, H. (2019). An evidential analytics for buried information in big data samples: Case study of semiconductor manufacturing. Information Sciences, 486, 190–203. doi: https://doi.org/10.1016/j.ins.2019.01.079
- Ramaji, I. J., Memari, A. M. (2018). Interpretation of structural analytical models from the coordination view in building information models. Automation in Construction, 90, 117–133. doi: https://doi.org/10.1016/j.autcon.2018.02.025
- Pérez-González, C. J., Colebrook, M., Roda-García, J. L., Rosa-Remedios, C. B. (2019). Developing a data analytics platform to support decision making in emergency and security management. Expert Systems with Applications, 120, 167–184. doi: https://doi.org/10.1016/j.eswa.2018.11.023
- Chen, H. (2018). Evaluation of Personalized Service Level for Library Information Management Based on Fuzzy Analytic Hierarchy Process. Procedia Computer Science, 131, 952–958. doi: https://doi.org/10.1016/j.procs.2018.04.233
- Chan, H. K., Sun, X., Chung, S.-H. (2019). When should fuzzy analytic hierarchy process be used instead of analytic hierarchy process? Decision Support Systems, 125, 113114. doi: https://doi.org/10.1016/j.dss.2019.113114
- Osman, A. M. S. (2019). A novel big data analytics framework for smart cities. Future Generation Computer Systems, 91, 620–633. doi: https://doi.org/10.1016/j.future.2018.06.046
- Gödri, I., Kardos, C., Pfeiffer, A., Váncza, J. (2019). Data analytics-based decision support workflow for high-mix low-volume production systems. CIRP Annals, 68 (1), 471–474. doi: https://doi.org/10.1016/j.cirp.2019.04.001
- Harding, J. L. (2013). Data quality in the integration and analysis of data from multiple sources: some research challenges. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-2/W1, 59–63. doi: https://doi.org/10.5194/isprsarchives-xl-2-w1-59-2013
- Papa, A., Shemet, Y., Yarovyi, A. (2021). Analysis of fuzzy logic methods for forecasting customer churn. Technology Audit and Production Reserves, 1 (2 (57)), 12–14. doi: https://doi.org/10.15587/2706-5448.2021.225285
- Gorelova, G. V. (2013). Cognitive approach to simulation of large systems. Izvestiya YuFU. Tekhnicheskie nauki, 3, 239–250.
- Lutsenko, I., Fomovskaya, E., Oksanych, I., Koval, S., Serdiuk, O. (2017). Development of a verification method of estimated indicators for their use as an optimization criterion. Eastern-European Journal of Enterprise Technologies, 2 (4 (86)), 17–23. doi: https://doi.org/10.15587/1729-4061.2017.95914
- Koshlan, A., Salnikova, O., Chekhovska, M., Zhyvotovskyi, R., Prokopenko, Y., Hurskyi, T. et. al. (2019). Development of an algorithm for complex processing of geospatial data in the special-purpose geoinformation system in conditions of diversity and uncertainty of data. Eastern-European Journal of Enterprise Technologies, 5 (9 (101)), 35–45. doi: https://doi.org/10.15587/1729-4061.2019.180197
- Mahdi, Q. A., Shyshatskyi, A., Prokopenko, Y., Ivakhnenko, T., Kupriyenko, D., Golian, V. et. al. (2021). Development of estimation and forecasting method in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 3 (9 (111)), 51–62. doi: https://doi.org/10.15587/1729-4061.2021.232718
- Poloziuk, K., Yaremenko, V. (2020). Neural networks and Monte-Carlo method usage in multi-agent systems for sudoku problem solving. Technology Audit and Production Reserves, 6 (2 (56)), 38–41. doi: https://doi.org/10.15587/2706-5448.2020.218427
- Аkanova, A., Kaldarova, M. (2020). Impact of the compilation method on determining the accuracy of the error loss in neural network learning. Technology Audit and Production Reserves, 6 (2 (56)), 34–37. doi: https://doi.org/10.15587/2706-5448.2020.217613
- Leoshchenko, S., Oliinyk, A., Subbotin, S., Zaiko, T. (2020). Usage of swarm intelligence strategies during projection of parallel neuroevolution methods for neuromodel synthesis. Technology Audit and Production Reserves, 5 (2 (55)), 12–17. doi: https://doi.org/10.15587/2706-5448.2020.214769
- Yaremenko, V., Syrotiuk, O. (2020). Development of a multi-agent system for solving domain dictionary construction problem. Technology Audit and Production Reserves, 4 (2 (54)), 27–30. doi: https://doi.org/10.15587/2706-5448.2020.208400
- Lakhno, V., Sagun, A., Khaidurov, V., Panasko, E. (2020). Development of an intelligent subsystem for operating system incidents forecasting. Technology Audit and Production Reserves, 2 (2 (52)), 35–39. doi: https://doi.org/10.15587/2706-5448.2020.202498
- Hoseini Alinodehi, S. P., Moshfe, S., Saber Zaeimian, M., Khoei, A., Hadidi, K. (2016). High-Speed General Purpose Genetic Algorithm Processor. IEEE Transactions on Cybernetics, 46 (7), 1551–1565. doi: https://doi.org/10.1109/tcyb.2015.2451595
- Hou, N., He, F., Zhou, Y., Chen, Y., Yan, X. (2018). A Parallel Genetic Algorithm With Dispersion Correction for HW/SW Partitioning on Multi-Core CPU and Many-Core GPU. IEEE Access, 6, 883–898. doi: https://doi.org/10.1109/access.2017.2776295
- Nobile, M. S., Cazzaniga, P., Besozzi, D., Colombo, R., Mauri, G., Pasi, G. (2018). Fuzzy Self-Tuning PSO: A settings-free algorithm for global optimization. Swarm and Evolutionary Computation, 39, 70–85. doi: https://doi.org/10.1016/j.swevo.2017.09.001
- Nugroho, E. D., Wibowo, M. E., Pulungan, R. (2017). Parallel implementation of genetic algorithm for searching optimal parameters of artificial neural networks. 2017 3rd International Conference on Science and Technology - Computer (ICST). doi: https://doi.org/10.1109/icstc.2017.8011867
- Bergel, A. (2020). Neuroevolution. Agile Artificial Intelligence in Pharo, 283–294. doi: https://doi.org/10.1007/978-1-4842-5384-7_14
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Vitalii Bezuhlyi, Volodymyr Oliynyk, Іgor Romanenko, Oleksandr Zhuk, Vasyl Kuzavkov, Oleh Borysov, Serhii Korobchenko, Eduard Ostapchuk, Taras Davydenko, Andrii Shyshatskyi
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.