Development of Network Training Complexes Using Fuzzy Models and Noise-Resistant Coding

Abstract

In this paper, an analysis of world experience was conducted and it was concluded that one of the ways to improve the efficiency of aviation security in the Russian Federation is to use modern network training complexes. A new approach to assessing the competence of aviation security screeners was proposed and tested, allowing taking into account the parameters of oculomotor activity and heart rate variability of test aviation security screeners, and differing from the existing approaches by using fuzzy classification models. According to the results of an experimental study, three different models were synthesized. The results of the comparison showed that the Sugeno model, trained using the ANFIS-algorithm, is more accurate than the Mamdani model and the linear regression model depends on the competence assessment of aviation security screeners. It described ways of addressing the important task of obtaining more precise relevant digital data in network training complexes using noise-resistant coding tools. It presented a model of a permutation decoder of a non-binary redundant code based on a lexicographic cognitive map. This model of a redundant code decoder uses cognitive data processing methods for completing permutation decoding procedures in order to protect remote control commands from the influence of destructive factors on the control process.