On the use of fuzzy neural networks in the framework of a risk-based approach in control and supervisory activities in civil aviation
https://doi.org/10.26467/2079-0619-2023-26-1-58-71
Abstract
A risk-oriented approach implemented in conducting control and supervisory activities in Civil Aviation organizations makes it possible to increase the effectiveness of such activities, the objectivity of assessments, to reduce costs and the additionalburden on business. The main provisions, regulating the activities of control and supervision bodies, including the issues of risk assessment, are generally specified in regulatory documents. However, uncertainty remains regarding the use of so-called risk indicators, which are designed to forecast risks for flight safety. Currently, there are no guidelines on the number and composition of such indicators, there are no methods to use them for the intended purpose. The article proposes a solution to this problem using elements of artificial intelligence. Based on the example of risk indicators distinctive for air traffic service organizations, the feasibility of forecasting the level of risk through a fuzzy (hybrid) neural network is shown. As is well known, such hybrid structures, combining neural networks and fuzzy logic, collect the best properties of both methods. The formation of a set of risk ndicators and initial data for network training is carried out with the involvement of qualified experts with extensive experience in flight safety management and control and supervisory activities. The trained network allows us to quantify a forecasted level of risk in an airline based on the identified risk indicators considering the degree of their manifestation. All the stages of building and using the network in the ANFIS editor of the MATLAB software package are shown. The proposed method can also be used in the flight safety management systems for various providers of aviation services.
About the Authors
R. А. Obraztsov
Central Interregional Territorial Administration Office of Air Transport of Central Regions of the Federal Air Transport Agency, Ministry of Transport of the Russian Federation
Russian Federation
Russian Federation
Roman A. Obraztsov, The Head of the Department for the Organization of the Use of Airspace and Radio Engineering Support of Flights
Moscow
V. D. Sharov
Moscow State Technical University of Civil Aviation
Russian Federation
Russian Federation
Valeriy D. Sharov, Doctor of Technical Sciences, Associate Professor, Professor of the Life and Flight Safety Chair
Moscow
References
1. Soloviev, A.I. (2017). Risk-oriented approach in the system of government control and supervision in the tax sphere. Ekonomika. Nalogi. Pravo, vol. 10, no. 6, pp. 139–146. (in Russian)
2. Avdiyskiy, V.I. & Bezdenezhnykh, V.M. (2016). The economic security of modern russia: the risk-based approach to its assurance. Ekonomika. Nalogi. Pravo, vol. 10, no. 3, pp. 6–13. (in Russian)
3. Agamagomedova, S.A. (2021). Riskoriented approach in the implementation of control and supervision activities: theoretical justification and problems of application. Siberian Law Review, vol. 18, no. 4, pp. 460–470. DOI: 10.19073/2658-7602-2021-18-4-460-470 (in Russian)
4. Ayres, I. & Braithwaite, J. (1992). Responsive regulation. transcending the deregulation debate. Oxford: Oxford University Press, 216 p.
5. Braithwaite, J. (2006). Responsive regulation and developing economies. World Development, vol. 34, no. 5, pp. 884–898. DOI: 10.1016/j.worlddev.2005.04.021
6. Ahmad, N. (2018). Responsive regulation and resiliency: the renewable fuel standard and advanced biofuels. Virginia Environmental Law Journal, vol. 36, issue 2, p. 40. Available at: https://ssrn.com/abstract=3106907 (accessed: 11.08.2022).
7. Kunien, V.A. & Uvarova, I.V. (2019). Towards a risk-orientated model of control and supervision activities in the civil aviation sphere. Economics and Management, no. 2 (160), pp. 59–68. (in Russian)
8. Mahutov, N.A., Pulikovskiy, K.B. & Shoygu, S.K. (2008). [Safety of Russia. Legal social economical scientific and technical aspects. Risk analysis and security management. (Guidelines)]. Moscow: MGF «Znaniye», 672 p. (in Russian)
9. Chertok, V.B. (2017). Towards a riskorientated model of control and supervision activities in the civil aviation sphere. Transport Rossiyskoy Federatsii, no. 6 (73), pp. 27–30. (in Russian)
10. Hadjimichael, M. (2009). A fuzzy expert system for aviation risk assessment. Expert Systems with Applications, vol. 36, no. 3, pp. 6512–6519. DOI: 10.1016/j.eswa.2008.07.081
11. Jenab, K. & Pineau, J. (2018). Automation of air traffic management using fuzzy logic algorithm to integrate unmanned aerial systems into the national airspace. International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 5, pp. 3169–3178. DOI: 10.11591/IJECE.V8I5.PP3169-3178
12. Sharov, V.D. & Vorobyov, V.V. (2017). Fuzzy risk assessment of aviation events. Civil Aviation High Technologies, vol. 20, no. 3, pp. 6–12.
13. Borisov, V.V., Kruglov, V.V. & Fedulov, A.S. (2007). [Fuzzy models and networks]. Moscow: Goryachaya liniya – Telekom, 284 p. (in Russian)
14. Osovskiy, S. (2002). [Neural networks for information processing]. Translated from Polish I.D. Rudinsky. Moscow: Finansy i statistika, 344 p. Available at: https://bookree.org/reader?file=555814&pg=4 (accessed: 12.08.2022). (in Russian)
15. Rutkovskaya, D., Pilinsky, M. & Rutkovsky, L. (2006). [Neural networks for information processing]. Translated from Polish I.D. Rudinsky. Moscow: Goryachaya liniya – Telekom, 452 p. (in Russian)
16. Jang, J-S.R. (1993). ANFIS: Adaptivenetwork-based fuzzy inference system. IEEE Transactions on System, Man, and Cybernetics, vol. 23, no. 3, pp. 665–685. DOI:10.1109/21.256541
17. Gorbachenko, V.I., Akhmetov, B.S. & Kuznetsova, O.Yu. (2019). [Intelligent systems: fuzzy systems and networks: Tutorial]. Moscow: Izdatelstvo Yurayt, 105 p. (in Russian)
18. Bogatikov, V.N., Dranishnikov, L.V. & Prorokov, A.E. (2011). [Construction of control systems based on neural networks: studyguide]. Apatity: Izdatelstvo KF PetrGU, 41 p. (in Russian)
Review
For citations:
Obraztsov R.А., Sharov V.D. On the use of fuzzy neural networks in the framework of a risk-based approach in control and supervisory activities in civil aviation. Civil Aviation High Technologies. 2023;26(1):58-71. https://doi.org/10.26467/2079-0619-2023-26-1-58-71
Views:
343
Email this article 
