Artificial Neural Networks for System Identification and Control

In an age where intelligent systems are transforming engineering practice, Artificial Neural Networks for System Identification and Control offers a clear roadmap to mastering artificial intelligence (AI)-driven modeling and control.From mathematical neuron models to adaptive Artificial Neural Network (ANN)-based controllers, this book combines theory, algorithms, and hands-on coding to help readers design and analyze intelligent systems.Rich with visual examples and real-world case studies, it demonstrates how neural networks outperform traditional control methods in handling nonlinearity, uncertainty, and dynamic system behavior. • Offers a practical and accessible guide to ANN-based system identification and control• Blends mathematical insight with real engineering applications• Provides Python-supported examples and visual case studies• Highlights key advances in nonlinear modeling and adaptive control design• Bridges the gap between theory, simulation, and real-world deploymentTh