Machine Learning for Complex and Unmanned Systems

This book highlights applications that use machine learning methods to enhance new developments in complex and unmanned systems. It includes two parts: the first one discusses machine learning applications in complex systems, and the second one collects applications of machine learning in unmanned systems. Each chapter provides guidelines and details of different methods that can be reproduced in hardware/software and discusses future research.

Format: Hardback
Length: 364 pages
Publication date: 21 February 2024
Publisher: Taylor & Francis Ltd

This comprehensive book delves into the realm of advanced applications that harness the power of machine learning techniques to revolutionize complex and unmanned systems. With a meticulous organization, the contents are structured to cover a wide range of applications, ranging from those that rely on a few specific methods to those that combine diverse approaches and explore their development and hardware/software implementation. The book is divided into two parts, each offering insightful perspectives. The first part focuses on machine learning applications in complex systems, primarily emphasizing their modeling and simulation, as well as their hardware implementation. It sheds light on recent advancements that have pushed the boundaries of these systems, enabling them to perform tasks with greater efficiency and accuracy. The second part delves into the realm of unmanned systems, encompassing optimization and case studies in submarines, drones, and robots. These chapters provide valuable insights into the practical applications of machine learning in these domains, highlighting its role in enhancing mission capabilities and improving operational efficiency. Additionally, the book includes chapters that address miscellaneous applications required by both complex and unmanned systems. These chapters delve into the fields of artificial intelligence, cryptography, embedded hardware, electronics, the Internet of Things, and healthcare. Each chapter offers detailed guidelines and outlines various methods that can be replicated in hardware/software, fostering a deeper understanding of their practical applications. Furthermore, the book highlights future research opportunities in these hot topic areas, providing valuable insights for graduate students, industrial and academic professionals seeking to explore the realm of machine learning in engineering.

Weight: 870g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032472249