ANALYSIS OF BIG DATA PROCESSING METHODS IN INTELLIGENT CONTROL SYSTEMS

Abstract

Intelligent data analysis is one of the key areas of artificial intelligence (AI) that uses AI methods and principles to automatically discover hidden patterns and knowledge in large datasets. The main idea of this field is to develop algorithms and models capable of automatically analyzing, classifying, clustering, and processing data in such a way that relationships and knowledge, which are not visible to the naked eye, can be revealed.

The goal of intelligent data processing, like AI in general, is to automate the data analysis process and uncover complex dependencies and knowledge in intelligent control systems. This approach is especially important when the volume of data is enormous, the information is diverse and complex, and complex mathematical and statistical methods are required to discover hidden patterns.

The article analyzes the methods of intelligent processing of big data that are created and accumulated in intelligent control systems. The development of information technologies requires rapid management of large volumes of unstructured data. With the emergence of an increasing number of IoT devices and sensors capable of interacting with the environment and with each other, a digital environment is emerging that contains huge volumes of diverse and changing data. Traditional methods of artificial intelligence and machine learning, developed for deterministic situations, are not suitable for such conditions. Since each device in an intelligent control system requires a large number of parameters, it is important that artificial intelligence can adapt and learn without a predetermined structure and parameters.

The article provides a brief overview of existing approaches and characteristics of Data Mining methods. An algorithm for the process of intelligent data processing is presented, which allows comparing the efficiency of building an intelligent control system.