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Full Description
In an age where intelligent systems are transforming engineering practice, Artificial Neural Networks for System Identification & Control offers a clear roadmap to mastering AI-driven modeling and control. From mathematical neuron models to adaptive 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.
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.
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 Content of Neural Network Topology 4. Back Propagation Algorithm 5. Dynamical Neural Networks 6. System Identification and Use of Neural Network 7. Neural Network for Applications of Control Theory 8. Condition Monitoring with Neural Nets
