Development of a method for assessment and forecasting of the radio electronic environment

• Published in: Eureka, Physics and Engineering (Online) no. 4; pp. 30 - 40
• Main Authors: Sova, Oleg, Shyshatskyi, Andrii, Salnikova, Olha, Zhuk, Oleksandr, Trotsko, Oleksandr, Hrokholskyi, Yaroslav
• Format: Journal Article
• Language: English
• Published: Tallinn Scientific Route OÜ 2021
• Subjects: Artificial neural networks; Cognitive models; Complexity; Data processing; Decision making; Efficiency; Forecasting; Fuzzy logic; Information processing; Learning; Mathematical models; Neural networks; Support systems; Systems analysis; Uncertainty
• ISSN: 2461-4254; 2461-4262
• DOI: 10.21303/2461-4262.2021.001940

Decision making support systems (DSS) are actively used in all spheres of human life. The system of the electronic environment analysis is not an exception. However, there are a number of problems in the analysis of the electronic environment, for example: the signals are analyzed in a complex electronic environment against the background of intentional and natural interference. Input signals do not match the standards, and their interpretation depends on the experience of the operator (expert), the completeness of additional information on a particular task (uncertainty condition). The best solution in this situation is found in the integration with the data of the information system analysis of the electronic environment, artificial neural networks and fuzzy cognitive models. Their advantages are also the ability to work in real time and quick adaptation to specific situations. The article develops a method for assessing and forecasting the electronic environment. Improving the efficiency of evaluation information processing is achieved through the use of evolving neuro-fuzzy artificial neural networks; learning not only the synaptic weights of the artificial neural network, the type and parameters of the membership function. The efficiency of information processing is also achieved through training in the architecture of artificial neural networks; taking into account the type of uncertainty of the information that has to be assessed; synthesis of rational structure of fuzzy cognitive model. It reduces the computational complexity of decision-making; has no accumulation of learning error of artificial neural networks as a result of processing the information coming to the input of artificial neural networks. The example of assessing the state of the electronic environment showed an increase in the efficiency of assessment at the level of 15–25 % on the efficiency of information processing