INTELLECTUAL MANAGEMENT BASED ON NEUROADAPTIVE MODELS

Abstract

This article provides an expanded analysis of the principles for constructing neuro-adaptive intelligent systems for forecasting and managing technological processes. Issues regarding the integration of artificial intelligence, machine learning, and adaptive control methods under conditions of uncertainty, nonlinearity, and multi-parameter nature of industrial facilities have been considered. A multi-level system architecture is proposed, including modules for data processing, forecasting, decision-making, and adaptation. Numerical modeling results are presented, confirming the effectiveness of the proposed approach.