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The object of research is the military radio communication system. Effective operation of routing protocols is possible only if there is reliable information about the network topology for network nodes, given that the mobility of individual nodes is insignificant in special purpose wireless networks. Because nodes in the network demonstrate the mobility property of the node groups. This paper solves the problem of the analysis (decomposition) of methods of protocols efficiency increase of dynamic routing in telecommunication networks with a possibility to self-organization. In the course of the research, the authors used the main provisions of the queuing theory, the theory of automation, the theory of complex technical systems and general scientific methods of cognition, namely analysis and synthesis. This research analyzes various methods to increase the efficiency of dynamic routing protocols. Energy efficiency methods focus on three main components in energy management: battery management, transmission energy management and system energy management methods. Reliable multicast has become indispensable for the successful deployment of special purpose wireless networks, such as in tactical military operations and emergency operations. The results of the research will be useful in: –development of new routing algorithms; –substantiation of recommendations for improving the efficiency of the route selection process in networks with the possibility of self-organization; –analysis of the electronic situation during hostilities (operations); –while creating promising technologies to increase the efficiency of mobile radio networks. Areas of further research will focus on the development of a methodology for the operational management of interference protection of intelligent military radio communication systems.

Artificial intelligence has become the backbone of modern decision support systems. This is why a complex method for finding solutions for neuro-fuzzy expert systems has been developed. The proposed complex method is based on a mathematical model for the analysis of the operational situation. The model makes it possible to determine the parameters of the analysis of the operational situation, their influence on the quality of assessment of the operational situation and to determine their number with units of measurement. An increase in the efficiency of information processing (error reduction) of the assessment is achieved by the use of evolving neuro-fuzzy artificial neural networks. Training of evolving neuro-fuzzy artificial neural networks is carried out by training not only synaptic weights of the artificial neural network, the type, parameters of the membership function, but also by applying the procedure for reducing the dimension of the feature space. The efficiency of information processing is also achieved by training the architecture of artificial neural networks; accounting for the type of uncertainty in the information to be assessed; work with both clear and fuzzy data. We achieved a reduction in computational complexity while making decisions; the absence of errors in training artificial neural networks as a result of processing information entering the input of artificial neural networks. The analysis of the operational situation as a whole occurs due to the improved clustering procedure, which allows working with both static and dynamic data. The proposed complex method was tested on the example of assessing the state of the operational situation. The mentioned example showed an increase in assessment efficiency at the level of 20–25% in terms of information processing efficiency