Identification of entrant’s abilities on the basis of Sugeno-type fuzzy inference systems
Abstract
In the conditions of effective training in aviation for dispatchers and pilots, it requires the use of infocommunication systems capable of working under conditions of fuzzy uncertainty in real time. The functioning of such systems is based on fuzzy inference systems. However, the development and implementation of these systems requires the creation of fuzzy knowledge bases. Therefore, special attention in this study is paid to the creation of a system of fuzzy inferences and the formation of a fuzzy knowledge base of this system. The result is a lozenge-type fuzzy inference system. The fuzzy knowledge base of the system contains the rules according to which, based on the results of test computer game problems of varying complexity, a conclusion is formed about the applicant’s ability to acquire knowledge and skills in a certain specialty.When developing the rules, both the results of passing different levels of professionally oriented computer test games were taken into account, and the interest of dispatchers and pilots was taken into account. Therefore, the proposed fuzzy rules of the knowledge base of the fuzzy inference system make it possible to assess not only the ability of the controller or pilot to solve certain problems. This dependence of the input dataset on time allows the implementation of a fuzzy inference system of the Sugeno type, using clear input data in the formation of inferences.
Keyword : pilots, dispatchers, membership function plot, priory knowledgebase, fuzzy rule
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Hadjimichael, M. (2009). A fuzzy expert system for aviation risk assessment. Expert Systems with Applications, 36(3), 6512–6519. https://doi.org/10.1016/j.eswa.2008.07.081
Honta, V. (2019). Gaming training technologies and evaluation as one of the innovative forms of the spatial awareness development. Management of Development of Complex Systems, 37, 138–143. http://urss.knuba.edu.ua/files/zbirnyk-37/24.pdf
Kaleta, W., & Skorupski, J. (2019). A fuzzy inference approach to analysis of LPV-200 procedures influence on air traffic safety. Transportation Research Part C: Emerging Technologies, 106, 264–280. https://doi.org/10.1016/j.trc.2019.07.001
Katasev, A. S. (2019). Neuro-fuzzy model for the formation of fuzzy rules for assessing the state of objects in conditions of uncertainty. Computer Research and Modeling, 11(3), 477–492. https://doi.org/10.20537/2076-7633-2019-11-3-477-492
Khalil, F., & Alam, H. M. (2020). Identification of Fintech driven operational risk events. Journal of the Research Society of Pakistan, 57(1), 75.
Mazandarani, M., & Xiu, L. (2022). Interval type-2 fractional fuzzy inference systems: Towards an evolution in fuzzy inference systems. Expert Systems with Applications, 189, 115947. https://doi.org/10.1016/j.eswa.2021.115947
Miyata, M., & Omori, T. (2018). Modeling emotion and inference as a value calculation system. Procedia Computer Science, 123, 295–301. https://doi.org/10.1016/j.procs.2018.01.046
Ng, A. K., & Ölçer, A. İ. (2012). A new human comfort model onboard a vessel based on Sugeno type fuzzy inference system. Ocean Engineering, 55, 116–124. https://doi.org/10.1016/j.oceaneng.2012.07.023
Peña, A., Bonet, I., Lochmuller, C., Chiclana, F., & Góngora, M. (2018). Flexible inverse adaptive fuzzy inference model to identify the evolution of operational value at risk for improving operational risk management. Applied Soft Computing, 65, 614–631. https://doi.org/10.1016/j.asoc.2018.01.024
Riabchun, Yu., Honcharenko, T., Honta, V., Chupryna, Kh., & Fedusenko, O. (2019). Methods and means of evaluation and development for prospective students’ spatial awareness. International Journal of Innovative Technology and Exploring Engineering, 8(11), 4050–4058. https://doi.org/10.35940/ijitee.K1532.0981119
Skorupski, J., & Uchroński, P. (2015). Fuzzy inference system for the efficiency assessment of hold baggage security control at the airport. Safety Science, 79, 314–323. https://doi.org/10.1016/j.ssci.2015.06.020
Skorupski, J., & Uchroński, P. (2016). Managing the process of passenger security control at an airport using the fuzzy inference system. Expert Systems with Applications, 54, 284–293. https://doi.org/10.1016/j.eswa.2015.11.014
Valizadeh, M., Braki, Z. A., Rashidi, R., Maghfourian, M., & Shenas, A. T. (2021). Fuzzy inference system and adaptive neuro-fuzzy inference system approaches based on spectrophotometry method for the simultaneous determination of salmeterol and fluticasone in binary mixture of pharmaceutical formulation. Optik, 244, 167592. https://doi.org/10.1016/j.ijleo.2021.167592
Wang, B., Zeng, J., Lin, S., & Bai, G. (2019). Multi-band images synchronous fusion based on NSST and fuzzy logical inference. Infrared Physics & Technology, 98, 94–107. https://doi.org/10.1016/j.infrared.2019.02.013
Wei, X. J., Zhang, D. Q., & Huang, S. J. (2021). A variable selection method for a hierarchical interval type-2 TSK fuzzy inference system. Fuzzy Sets and Systems, 438, 46–61. https://doi.org/10.1016/j.fss.2021.09.017
Yeremenko, B., Riabchun, Y., Ploskiy, V., Aznaurian, I., Mezzane, D., & Kryvinska, N. (2021). Intelligent information technologies implementation to the process of professional self-identification. In IntelITSIS’2021: 2nd International Workshop on Intelligent Information Technologies and Systems of Information Security (pp. 168–177). Khmelnytskyi, Ukraine.