Recommendations for Evaluating the Battle Efficiency of the Use of Troops Taking Into Account the Creation and Use of Different Options of Reconnaissance-strike Systems in Operations

The experience of using troops in joint force operations has shown the need to improve existing approaches to the principles and methods of combat employment of groupings of troops. Today, there is a clear tendency of transition from platform-centric to network-centric principles of warfare. One of the possible ways of transition to network-centric principles of warfare is the creation of reconnaissance-strike systems by situational integration of existing forces and means of reconnaissance, control and destruction into target systems in a single information space. The reconnaissance-strike systems will be built from a multitude of subsystems united by information relations, which function as a whole, on the basis of common principles and rules, with agreed basic requirements for their components. The integration of various forces and assets into target systems creates the prerequisites for the presence of several possible options for these systems, which have different assessments of the effectiveness of their use, the cost indicators of their creation, maintenance and use, as well as different time intervals for their creation and putting them on alert. One of the criteria for making a decision on the advisability of choosing one or another variant of a reconnaissance-strike systems is an indicator of an increase in the effectiveness of the combat employment of troops due to the principles of synergetic effect in the case of the creation and use of different versions of reconnaissance-strike systems. The practical implementation of proposals for the creation of new or improvement of existing reconnaissance-strike systems in the Armed Forces of Ukraine, as well as the development of methodological approaches for choosing the most rational version of these systems will require certain methodological recommendations to clarify approaches to assessing the combat effectiveness of the use of troops, taking into account the results of applying this or another version of the reconnaissance-strike systems in the operations of the Joint Forces of the Armed Forces of Ukraine. The author in the article proposes an algorithm for assessing the combat effectiveness of the use of troops, taking into account the creation and use of various options for reconnaissance-strike systems in an operation, on the basis of which the main recommendations for assessing combat effectiveness based on the use of reconnaissance-strike systems in an operation are formulated, and practical calculations of the effectiveness of various variants of reconnaissance-strike systems and a nomogram of the choice of a rational number of means of destruction of a reconnaissance-strike systems in conditions of a given number of means of destruction of the enemy is proposed

Ukrainian Military-economic Nuclear Bomb Has Already Exploded in Russia's Economy (Retrospective, Present and Future)

Purpose: to reveal a retrospective of Russia's preparations for war with Ukraine, to determine the main military and economic features and consequences of the Russian-Ukrainian war on Russia's economy, to form possible projected options for the state of Russia's economy from the development of scenarios and duration of hostilities between Russia and Ukraine. Design/Method/Approach: the main research methods are methods of statistical analysis, military-economic analysis, expert survey and forecasting methods.  Findings: analyzed the dynamics of the Russian economy before the Russian-Ukrainian war, determined the impact of sanctions against Russia on its economy today and predicted the state of recession in Russia's economy with different options for the development of hostilities and their duration, as well as possible military and economic risks for Russia in the future.  Theoretical implications: The main result of research on the subject of the article is the formation of a baseline database on the effects of the Ukrainian military-economic bomb on Russia's economy, taking into account the analytical retrospective results of Russia's participation in previous wars and armed conflicts. Paper type: descriptive and calculation-analytical

Development of an Algorithm to Train Artificial Neural Networks for Intelligent Decision Support Systems

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 processin