Abstract
This paper describes the development of the functional-neural-network (FN-network) formalism, which was worked out to create a range of information systems for the intelligent computer processing of heterogeneous data from various information sources and automated decision-making support systems. It considers the limitations of this formalism in solving the tasks when the time intervals at which a solution is searched for are given in the form of indefinite variables. A method is proposed for avoiding these limitations by entering knowledge about the internal structure of data (metaknowledge) on these intervals into the context of a solution and it is shown that the use of metaknowledge not only solves the problem, but also improves the efficiency of searching for a solution by attracting additional information from a knowledge base and the context of a task.
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