Scientific foundations and formalized methods for constructing automated control systems for technological processes and industries

Kondybayeva Almagul Baurzhanovna

Purpose: Development of a model for intelligent architectural design and structural design of human-machine systems designed to automate production and provide intellectual support for control processes and necessary data processing in organizational, technological and distributed control systems in various areas of technological production and other areas of human activity. Methods: The methodology is a singular value decomposition method for designing the core of a recommender system. The methodology includes scientific foundations and formalized methods for constructing automated process and production control systems, as well as technical preparation of production, etc., the developed model includes theoretical foundations and methods for mathematical modeling of organizational and technological systems and complexes, functional tasks and control objects and their algorithmization based on the construction of a recommender system using singular decomposition for the purpose of developing recommendations for choosing an architecture. Results: A recommender system model for designing various types of information systems architecture, developed on the basis of singular decomposition, singular decomposition, is presented. Conclusions: This method has a number of advantages and disadvantages. Because individual decomposition-based recommendation systems can produce variable results, the potential for implementing such models lies in the variety of recommendations offered. The model is trained automatically, i.e. the model processes the data, and itself identifies the production rules according to the presented characteristics based on the presented knowledge about the types of architectures for different types of information systems, which is an advantage for generating knowledge due to the possibility of automated scaling of the knowledge base.

Keywords:recommender system, recommendations, collaborative filtering, item-based, user-based, filtering content-based, knowledge-based filtering, hybrid recommendation systems, SVD.