Neural networks in control

Neural networks in control systems are computational models inspired by the human brain that learn to manage complex processes. They consist of interconnected layers of artificial neurons that process data to identify patterns and make decisions. By training on examples, neural networks can adapt to changing conditions, enabling systems like robots, vehicles, or industrial machines to operate efficiently and accurately. They excel at handling nonlinear, unpredictable environments where traditional control methods may struggle, making them valuable for advanced automation and intelligent system management.