Abstract
Analysis and assessment of the state of information objects is an urgent task in adaptive systems. Information about the current state of the system, its constituent components, the object of observation can be used in the decision-making process or in the implementation of control algorithms. However, an information object can have a complex structure or be characterized by many features, among which it is difficult to distinguish the main components. Therefore, an algorithm for analyzing and assessing the state of information objects is proposed, based on obtaining the compressed state of objects using neural networks. The resulting compressed state sufficiently characterizes the original object, but has a lower dimension. This can be used to speed up the analysis and assessment process and improve its accuracy in adaptive systems.
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