Hybrid Machine Learning Models for Intelligent Automation in Cyber Physical Systems to Improve Efficiency and Decision Making

The modern operational and complex environments of Cyber Physical Systems (CPS) need intelligent automated systems for effective management. The research analyzes combination methods of various machine learning paradigms to improve systematic decision processes within interconnected network systems. The framework we designed utilizes deep neural networks alongside reinforcement learning along with fuzzy logic systems. The proposed framework underwent evaluation by testing it through three industrial use cases that merged multiple sensor data streams. The hybrid system we built delivered better performance than standard single-model systems through enhancements of 87.4% for anomaly detection and surpasses traditional systems by 92.3% in predictive maintenance and 95.1% for resource optimization. The experimental results establish hybrid machine learning frameworks as optimal solutions for CPS domains which provide superior operational flexibility together with enhanced robustness but decreased computational requirements and enhanced real-time decision performance.