Development of methodological principles of routing in networks of special communication in the conditions of fire damage and radio electronic flow

Abstract

The object of research is a system of special communication. Decision making support systems (DMSS) are actively used in all spheres of human life. They are especially common in the processing of large data sets, forecasting processes, providing information support in the decision-making process by decision-makers. Systems of analysis of information transmission in special purpose radio communication systems are no exception. However, there are a number of problems in the transmission of information, namely: the transmission of information takes place in a complex electronic environment against the background of intentional and natural interference; elements of the radio communication system are the objects of primary fire damage due to high radio visibility for radio intelligence. The best solution in this situation is to integrate with the data of the information system analysis of the electronic environment, artificial neural networks and the ant algorithm. Their advantage is also the ability to work in real time and quickly adapt to specific situations. Therefore, in this paper the methodological principles of routing in special communication networks in the conditions of fire damage and electronic suppression are developed. Improving the efficiency of information processing (reducing error) evaluation is achieved through the use of evolving neuro-fuzzy artificial neural networks; learning not only the synaptic weights of the artificial neural network, but also the type and parameters of the membership function. 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 to be assessed; synthesis of rational structure of fuzzy cognitive model. It reduces the computational complexity of decision-making; absence of accumulation of an error of training of artificial neural networks as a result of processing of the information arriving on an input of artificial neural networks. The approbation of the use of the offered technique on the example of the estimation of information transfer in the conditions of influence of destabilizing factors is carried out. This example showed an increase in the efficiency of evaluation at the level of 15–25 % on the efficiency of information processing.