Partial Methodology for Assessing the Level of Methodological Training of Trainers During Combat Training of Tank Brigade During Combat Readiness

The experience of using tank brigade and units in the of anti-terrorist operation (ATO) and the operation of the Joint Forces (OJF), which are the main strike force of the Land Forces, indicates that their successful combat performance depends to a large extent on their combat capability. The level of preparedness of the brigade has a direct impact on combat capability.During combat renewal, combat training activities are conducted during which the training facilities acquire certain capabilities to perform combat missions. Due to the limited time involved in conducting combat training, methodological training of trainers has a significant impact on their level of training. This requires the search and implementation of new approaches to the quality of combat training activities by leaders of training in the course of combat readiness, which requires the development of a scientific and methodological apparatus to assess their level of methodological training. The article proposes a partial methodology for assessing the level of methodological training of leaders of training during combat training in the course of combat readiness, as part of a comprehensive methodology for assessing the effectiveness of combat tank training in the course of combat capability, which allows to take into account the impact of training leaders on the quality of training. The use of the proposed method allows the training subjects to quantify the level of methodological training of the trainers and to identify problems in the organization of their classes. The above methodology uses indicators that characterize the level of knowledge and skills of the head teacher in the subject of study, their experience in their classes and the availability of training courses to improve pedagogical skills

Improvement of the Method of Estimation and Forecasting of the State of the Monitoring Object in Intelligent Decision Support Systems

In order to objectively and completely analyze the state of the monitored object with the required level of efficiency, the method for estimating and forecasting the state of the monitored object in intelligent decision support systems was improved. The essence of the method is to provide an analysis of the current state of the monitored object and short-term forecasting of the state of the monitored object. Objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The novelty of the method is the use of an improved procedure for processing initial data in conditions of uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The procedure of forecasting the state of the monitored object allows for multidimensional analysis, accounting and indirect influence of all components of the multidimensional time series with their different time shifts relative to each other in conditions of uncertainty. The method allows increasing the efficiency of data processing at the level of 12–18 % using additional advanced procedures. The proposed method can be used in decision support systems of automated control systems (ACS DSS) for artillery units, special-purpose geographic information systems. It can also be used in ACS DSS for aviation and air defense and ACS DSS for logistics of the Armed Forces of Ukrain

Improvement of the Method of Estimation and Forecasting of the State of the Monitoring Object in Intelligent Decision Support Systems

In order to objectively and completely analyze the state of the monitored object with the required level of efficiency, the method for estimating and forecasting the state of the monitored object in intelligent decision support systems was improved. The essence of the method is to provide an analysis of the current state of the monitored object and short-term forecasting of the state of the monitored object. Objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The novelty of the method is the use of an improved procedure for processing initial data in conditions of uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The procedure of forecasting the state of the monitored object allows for multidimensional analysis, accounting and indirect influence of all components of the multidimensional time series with their different time shifts relative to each other in conditions of uncertainty. The method allows increasing the efficiency of data processing at the level of 12–18 % using additional advanced procedures. The proposed method can be used in decision support systems of automated control systems (ACS DSS) for artillery units, special-purpose geographic information systems. It can also be used in ACS DSS for aviation and air defense and ACS DSS for logistics of the Armed Forces of Ukrain

Development of A Method for Training Artificial Neural Networks for Intelligent Decision Support Systems

A method for training artificial neural networks for intelligent decision support systems has been developed. The method provides training not only of the synaptic weights of the artificial neural network, but also the type and parameters of the membership function, architecture and parameters of an individual network node. The architecture of artificial neural networks is trained if it is not possible to ensure the specified quality of functioning of artificial neural networks due to the training of parameters of an artificial neural network. The choice of architecture, type and parameters of the membership function takes into account the computing resources of the tool and the type and amount of information received at the input of the artificial neural network. The specified method allows the training of an individual network node and the combination of network nodes. The development of the proposed method is due to the need for training artificial neural networks for intelligent decision support systems, in order to process more information, with unambiguous decisions being made. This training method provides on average 10–18 % higher learning efficiency of artificial neural networks and does not accumulate errors during training. The specified method will allow training artificial neural networks, identifying effective measures to improve the functioning of artificial neural networks, increasing the efficiency of artificial neural networks through training the parameters and architecture of artificial neural networks. The method will allow reducing the use of computing resources of decision support systems, developing measures aimed at improving the efficiency of training artificial neural networks and increasing the efficiency of information processing in artificial neural network