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

for the development of anti-aircraft missile systems and complexes***** *****Central scientifically-reserch institute of arming and military equipment of the Armed Forces of Ukraine Povitrofloski ave.,

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processing of data coming from various technical devices of information extraction [1, 2]. The main advantage of information retrieval technologies over other information technologies is the ability to combine different types of information from different da-1. Introduction The rapid development of the number of information sources in the world significantly complicates the process of data extraction and necessitates their integration and

Nowadays, artificial intelligence has entered into all spheres of our life. The system of analysis of the electronic environment is not an exception. However, there are a number of problems in the analysis of the electronic environment, namely the signals. They are analyzed in a complex electronic environment against the background of intentional and natural interference. Also, the input signals do not match the standards due to the influence of different types of interference. Interpretation of signals depends on the experience of the operator, the completeness of additional information on a specific condition of uncertainty. The best solution in this situation is to integrate with the data of the information system analysis of the electronic environment and artificial neural networks. Their advantage is also the ability to work in real time and quick adaptation to specific situations. These circumstances cause uncertainty in the conditions of the task of signal recognition and fuzzy statements in their interpretation, when the additional involved information may be incomplete and the operator makes decisions based on their experience. That is why, in this article, an improved method for finding solutions for neuro-fuzzy expert systems of analysis of the electronic environment is developed. Improving the efficiency of information processing (reducing the error) of evaluation is achieved through the use of neuro-fuzzy artificial neural networks that are evolving and learning not only the synaptic weights of the artificial neural network, but also the type and parameters of the membership function. High efficiency of information processing is also achieved through training in the architecture of artificial neural networks by taking into account the type of uncertainty of the information that has to be assessed and work with clear and fuzzy products. This reduces the computational complexity of decision-making and absence of accumulation of an error of training of artificial neural networks as a result of processing of the arriving information on an input of artificial neural networks. The use of the proposed method was tested on the example of assessing the state of the electronic environment. This example showed an increase in the efficiency of assessment at the level of 20–25 % on the efficiency of the processing information

The complex methodology for processing different data in intelligent decision support systems is developed. This method is made to increase the efficiency of processing different data in intelligent decision support systems. The complex methodology consists of the following interrelated procedures: different data storing model; different data synchronization algorithm; different data separation algorithm; different data indexing algorithm. The model of storing different intelligence data, which is the basis of the methodology, differs in the presence of templates of intelligence objects and parameter templates of intelligence objects. Templates allow storing both unstructured different intelligence data and structured intelligence data according to a defined pattern, which reduces the time to access the data. In the different intelligence data storage model, a different intelligence data synchronization algorithm, different intelligence data separation algorithm and different intelligence data indexing algorithm are developed. The development of the proposed technique is due to the need to increase the efficiency of processing various information types in intelligent decision support systems with acceptable computational complexity. The proposed method allows increasing the efficiency of intelligent decision support systems through integrated processing of data circulating in them. The proposed method allows increasing the efficiency of information processing in decision support systems from 16 to 20 % depending on the amount of information about the monitoring object.

The method of estimation and forecasting in intelligent decision support systems was developed. The essence of the method is the analysis of the current state of the object and short-term forecasting of the object state. Objective and complete analysis is achieved by using improved fuzzy temporal models of the object state and an improved procedure for processing the original data under uncertainty. Also, the possibility of objective and complete analysis is achieved through an improved procedure for forecasting the object state and an improved procedure for learning evolving artificial neural networks. The concepts of fuzzy cognitive model are related by subsets of influence fuzzy degrees, arranged in chronological order, taking into account the time lags of the corresponding components of the multidimensional time series. The method is based on fuzzy temporal models and evolving artificial neural networks. The peculiarity of the method is the possibility of taking into account the type of a priori uncertainty about the object state (full awareness of the object state, partial awareness of the object state and complete uncertainty about the object state). The possibility to clarify information about the object state is achieved using an advanced training procedure. It consists in training the synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The object state forecasting procedure allows conducting multidimensional analysis, consideration, and indirect influence of all components of a multidimensional time series with their different time shifts relative to each other under uncertainty. The method provides an increase in data processing efficiency at the level of 15–25% using additional advanced procedures.

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