Abstract
Decision making support systems (DSS) are actively used in all spheres of human life. The system of the electronic environment analysis is not an exception. However, there are a number of problems in the analysis of the electronic environment, for example: the signals are analyzed in a complex electronic environment against the background of intentional and natural interference. Input signals do not match the standards, and their interpretation depends on the experience of the operator (expert), the completeness of additional information on a particular task (uncertainty condition). The best solution in this situation is found in the integration with the data of the information system analysis of the electronic environment, artificial neural networks and fuzzy cognitive models. Their advantages are also the ability to work in real time and quick adaptation to specific situations. The article develops a method for assessing and forecasting the electronic environment.
 Improving the efficiency of evaluation information processing is achieved through the use of evolving neuro-fuzzy artificial neural networks; learning not only the synaptic weights of the artificial neural network, the type and parameters of the membership function. The efficiency of information processing is also achieved through training in the architecture of artificial neural networks; taking into account the type of uncertainty of the information that has to be assessed; synthesis of rational structure of fuzzy cognitive model. It reduces the computational complexity of decision-making; has no accumulation of learning error of artificial neural networks as a result of processing the information coming to the input of artificial neural networks. The example of assessing the state of the electronic environment showed an increase in the efficiency of assessment at the level of 15–25 % on the efficiency of information processing
Highlights
Decision making support systems (DMSS) are actively used in all spheres of human life.The creation of intelligent DMSS has become a natural continuation of the widespread use of the classical type DMSS
While analyzing the tasks of the radio electronic environment (REE) there is a number of problematic issues, for example: 1. Signals are analyzed in a complex electronic environment against the background of different origins interference
A multilayer perceptron (MLP), a radial-base neural network (RBNN), and an evolving artificial neural network were used to compare the quality of the prediction
Summary
Decision making support systems (DMSS) are actively used in all spheres of human life.The creation of intelligent DMSS has become a natural continuation of the widespread use of the classical type DMSS. Intelligent DMSS have been widely used to solve specific military tasks [1, 2]:. – planning the deployment, operation of communication systems and data transmission; – automation of troops and weapons control; – collection, processing and generalization of intelligence information about the intelligence objects state, etc. One of such tasks for using intelligent DMSS is the analysis and forecasting of the electronic situation, which is reduced to a multi-criteria analysis of alternatives, given the large number and diversity of troops (forces) groups. While analyzing the tasks of the radio electronic environment (REE) there is a number of problematic issues, for example: 1. While analyzing the tasks of the radio electronic environment (REE) there is a number of problematic issues, for example: 1. Signals are analyzed in a complex electronic environment against the background of different origins interference
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