Advanced ANN-Based Control Systems for Smart Monitoring and Management of Industrial Processes

Abstract

The rapid evolution of industrial automation has necessitated the development of intelligent control systems capable of handling complex, nonlinear, and dynamic processes. Artificial Neural Networks (ANNs) have emerged as powerful tools for modeling, control, and monitoring in industrial environments due to their ability to learn from data and adapt to changing conditions. This research presents a comprehensive model of an ANN-based control system integrated with global systems for effective monitoring and management of industrial processes. The proposed framework combines neural computation, real-time data acquisition, and global communication systems to enhance process efficiency, fault detection, and decision-making. The study explores system architecture, algorithmic design, and operational workflows, supported by theoretical and technical analysis. The findings demonstrate that ANN-based systems significantly improve accuracy, adaptability, and robustness compared to traditional control mechanisms. The paper concludes by highlighting the implications of integrating intelligent systems into industrial automation and suggests future research directions focusing on scalability, hybrid models, and advanced learning techniques.