On the use of fuzzy neural networks in the framework of a risk-based approach in control and supervisory activities in civil aviation

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.