and Methodological Support for the Development and Implementation of Programs for the Development of Weapons And Military Equipment and the State Defense Order** R . Z h y v o t o v s k y i PhD, Senior Researcher, Head of Research Department Research Department of the Development of Anti-Aircraft Missile Systems and Complexes** I u . R e p i l o Doctor of Military Sciences, Professor Department of Missile Troops and Artillery*** O . Z a b o l o t n y i PhD, Associate Professor, Leading Researcher Center of Military Strategic Studies*** O . S y m o n e n k o

The algorithm to train artificial neural networks for intelligent decision support systems has been constructed. A distinctive feature of the proposed algorithm is that it conducts training not only for synaptic weights of an artificial neural network, but also for the type and parameters of membership function. In case of inability to ensure the assigned quality of functioning of artificial neural networks due to training of parameters of artificial neural network, the architecture of artificial neural networks is trained. The choice of the architecture, type and parameters of membership function occurs taking into consideration the computation resources of the facility and taking into consideration the type and the amount of information entering the input of an artificial neural network. In addition, when using the proposed algorithm, there is no accumulation of an error of artificial neural networks training as a result of processing the information entering the input of artificial neural networks.Development of the proposed algorithm was predetermined by the need to train artificial neural networks for intelligent decision support systems in order to process more information given the unambiguity of decisions being made. The research results revealed that the specified training algorithm provides on average 16–23 % higher the efficiency of training artificial neural networks training that is on average by 16–23 % higher and does not accumulate errors in the course of training. The specified algorithm will make it possible to conduct training of artificial neural networks; to determine effective measures to enhance the efficiency of functioning of artificial neural networks. The developed algorithm will also enable the improvement of the efficiency of functioning of artificial neural networks due to training the parameters and the architecture of artificial neural networks. The proposed algorithm reduces the use of computational resources of decision support systems. The application of the developed algorithm makes it possible to work out the measures aimed at improving the effectiveness of training artificial neural networks and to increase the efficiency of information processing

During the research of the use issue of samples of weapons and military equipment in operations, a significant correlation was established between the predicted effectiveness of such use and the characteristics of the quantitative and qualitative composition of samples of weapons and military equipment. Taking this into account, it is clear that while preparing any operation, it would be desirable to have such a basis of the composition of samples of weapons and military equipment, which is well-founded and can only be adjusted in the conditions of a certain operation. The objects of the research are the samples of weapons and military equipment that are the part of the groups of troops (forces). However, the results of the analysis show that the existing methods for substantiating the composition of samples of weapons and military equipment need improvement. First of all, this concerns the determination of the basic (support) version of the composition of samples of weapons and military equipment (WME). As in the existing methods, the basic (support) version of the composition is not determined, it is chosen by comparing the composition of one's troops and the enemy's troops according to their combat potential. This approach does not provide an opportunity to compare the groups, taking into account the specifics of the use of their striking equipment and to create the necessary balance of forces at all stages of the operation. Taking into account the above, we conducted the researches that made it possible to determine that with certain proportions, characteristic of the organizational structure of the absolute majority of military formations, there is a close to linear relationship between the number of their personnel and WME and combat potential. Based on the research results, an improved method of determining the basic (support) composition of samples of weapons and military equipment in operation is proposed.