The scientific degree thesis is devoted to solve the task to create an efficient modeling technology for network topology of peer-to-peer mobile self-adaptive tactical military radio network and to manage the changing performance indicators of such radio network under radio frequency interference and defense. The scientific thesis first time offered a brand new topology differing from existing ones, that researches network behavior under circumstances of interference and radiofrequency defense. Innovative intellect management of mobile radio network node were introduced: search adjustment of the noise level or interference signal on entry of communicator and connectivity control of the radio network nodes. Main difference of current intellectual system is mechanism of data/knowledge storage and processing (knowledge base block) for efficient activities in uncertain (lack of information) and random circumstance. The knowledgebase contains the control system, it’s goals and management principles, decision making structure and the control object itself. The control system can be contributed with learning sub-system, that generalizes the accumulated experience. The subsystem for control, gathering, storage and processing of data measures mobile nodes and general radio network parameters. The decision making subsystem was build thinking about unification of control functions into independent groups to separate network management on subsystems and ensure easier math modeling of network management. The new gradient approach of self-adapted radio network was proposed, that differs from known methods by gradient setting of neighboring nodes weight and search of close path in network affected by interference. Dijkstra algorithm is a search procedure of the shortest path at weighted oriented graph. Algorithm works by steps, starting from first radio network node: on each step it refers to one node, and reduces marks and stops execution when all radio network nodes are visited. Dijkstra algorithm is resourceful, but given the knowledge of network topology and path to necessary peak, the router always knows an alternative route to the required node, in case of fall of any node of the path. Self-learning is a key feature for solving complex problems, that cannot be solved in usual way. The difficulty of constructing such network is to choose invariant features for describing of input data so the differences are caused only by random factors, such as noise. In this case, the informative features will be the vector representation of the symbols on which the noise component or interference was applied. Among the major types of neuro networks, including deep learning networks, the BP (back propagation) structure of neuro network is widely used, because it has features of self-adaptation, and recognition is computation-efficient. The algorithm of non-linear optimization (Levenberg–Marquardt algorithm) which is applied for search of minimal strategy – linear approximation and gradient descent. According to the simulation procedure, we determine the neural network BP with three layers. The initial structure has two layers, the number of neurons in the first layer is 33, and in the second - 27, which corresponds to the number of network outputs. The network training function allows to assess the quality of network configuration by constructing a regression line in which the proportionality factor allows to determine the degree of correlation between input and output data. In this case, there is a high degree of correlation between input and output data, R = 0.999. Training in this example results in an error of 1.52 · 10-5, due to the complexity of the output data. The learning took only eight epochs. The BP multilayer neural network self-adapting algorithm is a controlled algorithm. In fact, it's an iterative method of gradient search for the best parameters in these conditions, which is characterized by the simplicity of the classification task in terms of "input-output" and reliability. New results of radio network modeling are obtained. On the one hand, they differ from the known ones in that the radio network modeling is performed on the basis of gradient learning algorithm. On the other hand, the results are confirmed by theoretical researches and practical results. The proposed geo information technology of automated data processing with a graphical representation of the radio network topology using the geographic information system ArcGIS-10 of the American company ESRI, which allows to assess the stability of the network structure in dynamic change and identify the limits of stable connectivity of radio switching nodes. This approach is a new variation that expands the boundaries of solving the problem of traffic distribution and noise immunity of the radio network, taking into account the structure of the network topology.
