Systems Engineering Neural Networks 1st Edition

Grab the future of intelligent systems with Systems Engineering Neural Networks, 1st Edition by Alessandro Migliaccio and Giovanni Iannone. This authoritative work bridges rigorous systems engineering principles with modern neural network design, delivering a focused, practical roadmap for engineers, researchers, and advanced students.

Explore a clear progression from foundational theory to real-world application: mathematical models and architectures, training and validation techniques, stability and robustness analysis, and strategies for integrating neural networks into complex engineered systems. Written with clarity and technical precision, the book balances formalism with actionable guidance—ideal for control engineers, AI practitioners, and systems architects designing reliable, explainable solutions.

Readers will appreciate concise derivations, insightful examples, and cross-disciplinary perspectives that connect machine learning with systems thinking. The content highlights use cases across automation, aerospace, robotics, and industrial IoT, making it relevant to professionals and academics worldwide—from Europe and North America to Asia and beyond.

Whether you’re building predictive controllers, implementing adaptive filters, or developing fault-tolerant architectures, this edition equips you with the tools to design scalable, trustworthy neural-network-based systems. Technical yet accessible, it supports curriculum development and professional reference needs alike.

Own a practical, future-facing resource that strengthens both theoretical understanding and engineering practice. Add Systems Engineering Neural Networks, 1st Edition to your library—order your copy today and start transforming complex system challenges into intelligent, reliable solutions.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.