FRACTAL CYBERNETIC MODEL OF KNOWLEDGE

ФРАКТАЛЬНО КИБЕРНЕТИЧЕСКАЯ МОДЕЛЬ ЗНАНИЕ

Abstract

The paper proposes a new architecture of the fractal cybernetic knowledge model (FCKM), designed to formalize the processes of converting information into data, information and knowledge in intelligent systems. The relevance of the research is due to the need to develop new approaches to the organization of artificial intelligence that can function effectively in conditions of constant growth in the volume of heterogeneous data and information uncertainty. Unlike modern neural network technologies, which are mainly focused on increasing computing resources and processing large amounts of unstructured information, the proposed approach is based on the semantic representation of knowledge and mechanisms for intelligent management of information flows.

The article examines the semantic relationship between the concepts of "information", "data", "information" and "knowledge", and also suggests a formal scheme for their transformation within a single fractal structure. The developed model includes feedback mechanisms that ensure self-organization, adaptation, and intelligent filtering of incoming information. Special attention is paid to the role of associative entities that perform the functions of contextual processing and selection of relevant information. A mathematical description of the model's functioning is presented and an example of its practical application is considered.

The scientific novelty of the work lies in the development of a fractal principle of knowledge organization that provides recursive management of the processes of information formation and interpretation. The practical significance of the research lies in the possibility of using the proposed model in the creation of intelligent systems, expert complexes, decision support systems, robotic platforms and promising artificial intelligence architectures.