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Full Description
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 deployment
This book is intended for engineers, researchers, and advanced students seeking to apply artificial intelligence to control theory, robotics, and signal processing and to design smarter, more adaptive engineering systems.
Contents
1. Introduction. 2. Artificial Neuron and Its Mathematical Model. 3. Information Flow or Information Content of NN Structure. 4. Backpropagation Algorithm. 5. Dynamical Neural Networks. 6. System Identification and Use of Neural Networks. 7. Neural Network for Applications of Control Theory. 8. Condition Monitoring with Neural Nets.
