ISBN: 9781041302773 - learning-enabled autonomous systems: control, verification, and monitoring (12 resultados)

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Condición: New. Dr. Jianglin Lan is a Lecturer in Autonomous Systems and Leverhulme Early Career Fellow at the James Watt School of Engineering, University of Glasgow, and an Honorary Research Fellow at Imperial College London. He received his PhD in Control and Intelligen.

Buch. Condición: Neu. Neuware - This book examines how to design intelligent systems that are not only adaptive but also safe and reliable. This book bridges the gap between traditional control theory and modern data-driven learning, presenting a unified framework for creating autonomous systems capable of robust decision-making in uncertain and dynamic environments.Learning-Enabled Autonomous Systems: Control, Verification, and Monitoring stands out as a unique resource for designing trustworthy autonomous systems. It introduces data-driven control methods that allow systems to learn from real-world data, enabling adaptability and intelligence without sacrificing mathematical rigor. The book explores advanced control strategies for nonlinear systems, ensuring computational practicality for real-world applications. It also provides innovative techniques for verifying neural network controllers and safeguarding system performance through runtime monitoring frameworks. By uniting control theory, machine learning, and systems verification, this book offers a holistic approach to creating systems that are not only intelligent but also resilient, transparent, and dependable. It includes case studies, algorithmic insights, and design guidelines that connect theoretical principles to hands-on engineering practice.This book is tailored for graduate students, researchers, and practitioners in control systems, robotics, artificial intelligence, and systems engineering. It is ideal for those seeking to deepen their understanding of learning-enabled control systems, whether for academic study or real-world application.…

Hardcover. Condición: new. Hardcover. This book examines how to design intelligent systems that are not only adaptive but also safe and reliable. This book bridges the gap between traditional control theory and modern data-driven learning, presenting a unified framework for creating autonomous systems capable of robust decision-making in uncertain and dynamic environments.Learning-Enabled Autonomous Systems: Control, Verification, and Monitoring stands out as a unique resource for designing trustworthy autonomous systems. It introduces data-driven control methods that allow systems to learn from real-world data, enabling adaptability and intelligence without sacrificing mathematical rigor. The book explores advanced control strategies for nonlinear systems, ensuring computational practicality for real-world applications. It also provides innovative techniques for verifying neural network controllers and safeguarding system performance through runtime monitoring frameworks. By uniting control theory, machine learning, and systems verification, this book offers a holistic approach to creating systems that are not only intelligent but also resilient, transparent, and dependable. It includes case studies, algorithmic insights, and design guidelines that connect theoretical principles to hands-on engineering practice.This book is tailored for graduate students, researchers, and practitioners in control systems, robotics, artificial intelligence, and systems engineering. It is ideal for those seeking to deepen their understanding of learning-enabled control systems, whether for academic study or real-world application. This book bridges control theory and data-driven learning for designing adaptive, safe, and reliable autonomous systems. It covers advanced control strategies and runtime monitoring with case studies connecting theory to practice. Ideal for students, researchers, and practitioners in control systems, robotics, AI, and engineering. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

HRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Condición: new. Hardcover. This book examines how to design intelligent systems that are not only adaptive but also safe and reliable. This book bridges the gap between traditional control theory and modern data-driven learning, presenting a unified framework for creating autonomous systems capable of robust decision-making in uncertain and dynamic environments.Learning-Enabled Autonomous Systems: Control, Verification, and Monitoring stands out as a unique resource for designing trustworthy autonomous systems. It introduces data-driven control methods that allow systems to learn from real-world data, enabling adaptability and intelligence without sacrificing mathematical rigor. The book explores advanced control strategies for nonlinear systems, ensuring computational practicality for real-world applications. It also provides innovative techniques for verifying neural network controllers and safeguarding system performance through runtime monitoring frameworks. By uniting control theory, machine learning, and systems verification, this book offers a holistic approach to creating systems that are not only intelligent but also resilient, transparent, and dependable. It includes case studies, algorithmic insights, and design guidelines that connect theoretical principles to hands-on engineering practice.This book is tailored for graduate students, researchers, and practitioners in control systems, robotics, artificial intelligence, and systems engineering. It is ideal for those seeking to deepen their understanding of learning-enabled control systems, whether for academic study or real-world application. This book bridges control theory and data-driven learning for designing adaptive, safe, and reliable autonomous systems. It covers advanced control strategies and runtime monitoring with case studies connecting theory to practice. Ideal for students, researchers, and practitioners in control systems, robotics, AI, and engineering. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Hardcover. Condición: new. Hardcover. This book examines how to design intelligent systems that are not only adaptive but also safe and reliable. This book bridges the gap between traditional control theory and modern data-driven learning, presenting a unified framework for creating autonomous systems capable of robust decision-making in uncertain and dynamic environments.Learning-Enabled Autonomous Systems: Control, Verification, and Monitoring stands out as a unique resource for designing trustworthy autonomous systems. It introduces data-driven control methods that allow systems to learn from real-world data, enabling adaptability and intelligence without sacrificing mathematical rigor. The book explores advanced control strategies for nonlinear systems, ensuring computational practicality for real-world applications. It also provides innovative techniques for verifying neural network controllers and safeguarding system performance through runtime monitoring frameworks. By uniting control theory, machine learning, and systems verification, this book offers a holistic approach to creating systems that are not only intelligent but also resilient, transparent, and dependable. It includes case studies, algorithmic insights, and design guidelines that connect theoretical principles to hands-on engineering practice.This book is tailored for graduate students, researchers, and practitioners in control systems, robotics, artificial intelligence, and systems engineering. It is ideal for those seeking to deepen their understanding of learning-enabled control systems, whether for academic study or real-world application. This book bridges control theory and data-driven learning for designing adaptive, safe, and reliable autonomous systems. It covers advanced control strategies and runtime monitoring with case studies connecting theory to practice. Ideal for students, researchers, and practitioners in control systems, robotics, AI, and engineering. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…