Springer Introduction to Hybrid Intelligent Networks: Modeling, Communication, and Control

This book covers the fundamental principles, new theories and methodologies, and potential applications of hybrid intelligent networks. Chapters focus on  hybrid neural networks and networked multi-agent networks, including their communication, control and optimization synthesis. This text also provides a succinct but useful guideline for designing neural network-based hybrid artificial intelligence for brain-inspired computation systems and applications in the Internet of Things. Artificial Intelligence has developed into a deep research field targeting robots with more brain-inspired perception, learning, decision-making abilities, etc. This text devoted to a tutorial on hybrid intelligent networks that have been identified in nature and engineering, especially in the brain, modeled by hybrid dynamical systems and complex networks, and have shown potential application to brain-inspired intelligence. Included in this text are impulsive neural networks, neurodynamics, multiagent networks, hybrid dynamics analysis, collective dynamics, as well as hybrid communication, control and optimization methods. Graduate students who are interested in artificial intelligence and hybrid intelligence, as well as professors and graduate students who are interested in neural networks and multiagent networks will find this textbook a valuable resource. AI engineers and consultants who are working in  wireless communications and networking will want to buy this book. Also, professional and academic institutions in universities and Mobile vehicle companies and engineers and managers who concern humans in the loop of IoT will also be interested in this book. About the Author Zhi-Hong Guan received the Ph.D. degree in automatic control theory and applications from the South China University of Technology, Guangzhou, China in 1994. He was a Full Professor of mathematics and automatic control with the Jianghan Petroleum Institute, Jingzhou, China in 1994. He has been with the Huazhong University of Science and Technology since 1997, where currently he is a Huazhong Leading Professor. Since 1999, he has held visiting positions at Harvard University, USA, the Central Queensland University, Australia, the Loughborough University, U.K., the National University of Singapore, the University of Hong Kong, and the City University of Hong Kong. He was awarded the Natural Science Award (First Class) from the Ministry of Education of China in 2005 and the Natural Science Award (First Class) from the Hubei Province of China in 2014. His research interests include complex systems and complex networks, impulsive and hybrid control systems, networked control systems, multi-agent systems, networked robotic systems, complex smart grids, neural networks, and genetic regulatory networks. Bin Hu received the Ph.D. degree in Control Science and Engineering from the Huazhong University of Science and Technology (HUST), Wuhan, China, in 2015. She is currently an Associate Professor with the Wuhan National Laboratory for Optoelectronics, HUST. Her current research interests include distributed control and optimization of multiagent networks, hybrid control systems, and neural network and artificial intelligence.   Xuemin (Sherman) Shen (M’97–SM’02–F’09) received the Ph.D. degree in electrical engineering from Rutgers University, New Brunswick, NJ, USA, in 1990. He is currently a University Professor with the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada. His research focuses on resource management in interconnected wireless/wired networks, wireless network security, social networks, smart grid, and vehicular ad hoc and sensor networks. He is a registered Professional Engineer of Ontario, Canada, an Engineering Institute of Canada Fellow, a Canadian Academy of Engineering Fellow, a Royal Society of Canada Fellow, and a Distinguished Lecturer of the IEEE Vehicular Technology Society a