Publisher Description

This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the elds of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks.  •  Species the importance of efficient theoretical optimization and learning methods in    dealing with emerging problems in the context of interdependent networks  •  Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks  •  Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks  M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.

Back Cover

This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the Fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. * SpeciFies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks * Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks * Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.

Table of Contents

Introduction.- Interdependent Complex Networks: Tale of IoT-based Smart Cities.- Deep Learning Algorithms for Energy Systems.- Distributed Algorithms for Interdependent Networks.- Online Optimization Learning for Interdependent Complex Networks.- Deep Learning Algorithms for Ramp Rate Prediction in Unit Commitment.- Networked Control Systems: Case Study of Unmanned Aerial Vehicle.- Conclusion.

Feature

SpeciFies the importance of ecient theoretical methods in dealing with problems in the context of interdependent networks Provides a comprehensive investigation of recently developed algorithms for largescale networks Presents basics and mathematical foundations needed to analyze and address the interdependent complex networks

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