Analysis and Control of Output Synchronization for Complex Dynamical Networks
Autor Jin-Liang Wang, Huai-Ning Wu, Tingwen Huang, Shun-Yan Renen Limba Engleză Hardback – 23 aug 2018
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Specificații
ISBN-13: 9789811313516
ISBN-10: 9811313512
Pagini: 220
Ilustrații: XIV, 216 p. 97 illus., 28 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.5 kg
Ediția:1st ed. 2019
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore
ISBN-10: 9811313512
Pagini: 220
Ilustrații: XIV, 216 p. 97 illus., 28 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.5 kg
Ediția:1st ed. 2019
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore
Cuprins
Introduction.- Output Synchronization Criteria for Impulsive CDNs with Time-varying Delay.- Passivity and Output Synchronization of CDNs with Fixed and Adaptive Coupling Strength.- Analysis and Control of Output Synchronization in Directed and Undirected CDNs.- Output Synchronization in CNNs with and without External Disturbances.- Local and Global Exponential Output Synchronization of CDDNs.- Adaptive output synchronization of CDDNs with output coupling.- Pinning Synchronization of CDNs With Multi-Weights.- Analysis and Pinning Control for Output Synchronization and H-Infinity Output Synchronization of Multi-weighted Complex Networks.
Notă biografică
Jin-Liang Wang received the Ph.D. degree in control theory and control engineering from the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China, in 2014. In January 2014, he joined the School of Computer Science & Software Engineering, Tianjin Polytechnic University, Tianjin, China. He was a Program Aid with Texas A & M University at Qatar, Doha, Qatar, in 2014, for two months. From June 2015 to July 2015，from July 2016 to August 2016 and from June 2017 to September 2017, he was a Postdoctoral Research Associate with Texas A & M University at Qatar, Doha, Qatar.
Dr. Wang serves as an Associate Editor of the Neurocomputing. His current research interests include complex networks, coupled reaction-diffusion neural networks, and distributed parameter systems.
Huai-Ning Wu was born in Anhui, China, on November 15, 1972. He received the B.E. degree in automation from Shandong Institute of BuildingMaterials Industry, Jinan, China and the Ph.D. degree in control theory and control engineering from Xi’an Jiaotong University, Xi’an, China, in 1992 and 1997, respectively.
From August 1997 to July 1999, he was a Post-doctoral Research Fellow with the Department of Electronic Engineering at Beijing Institute of Technology, Beijing, China. Since August 1999, he has been with the School of Automation Science and Electrical Engineering, Beihang University (formerly Beijing University of Aeronautics and Astronautics), Beijing. From December 2005 to May 2006, he was a Senior Research Associate with the Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong (CityU), Kowloon, Hong Kong. From October to December during 2006-2008 and from July to August in 2010, 2011 and 2013, he was a Research Fellow in CityU. He is currently a Professor with Beihang University, the Distinguished Professor of Yangtze River Scholar, Ministry of Education of China. He has published more than 90 SCI journal papers including 60 articles of the IEEE Transactions and 9 articles of the journal: Automatica. He has won the Second Prize of Natural Science of China and obtained China National Funds for Distinguished Young Scientists. His current research interests include robust control, fault-tolerant control, distributed parameter systems, and fuzzy/neural modeling and control.
Dr. Wu serves as an Associate Editor of the IEEE Transactions on Fuzzy Systems, and the IEEE Transactions on Systems, Man & Cybernetics: Systems.
Tingwen Huang received his B.S. degree in mathematics from Southwest Normal University, Chongqing, China, in 1990, M.S. degree in applied mathematics from Sichuan University, Chengdu, China, in 1993 and Ph.D. degree in mathematics from Texas A & M University, College Station, Texas, USA, in 2002. He was a Lecturer with Jiangsu University, Zhenjiang, China, from 1994 to 1998, and a Visiting Assistant Professor with Texas A & M University, College Station, TX, USA, in 2003. From 2003 to 2009, he was an Assistant Professor, from 2009 to 2013, an Associate Professor, and, since 2013, a Professor with Texas A & M University at Qatar, Doha, Qatar. His current research interests include neural networks, coupled reaction-diffusion neural networks, complex networks, chaos and dynamics of systems, and operator semi-groups and their applications. He has authored or co-authored about 30 IEEE Trans journal papers.
Dr. Huang serves as an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems, and the IEEE Transactions on Cybernetics.
Shun-Yan Ren received the B.S. degree in mathematics from Langfang Teachers University, Langfang, China, in 2007. She is currently pursuing the Ph.D. degree in mechanical design and theory with the School of Mechanical Engineering, Tianjin Polytechnic University, Tianjin, China. Her research interests include stability, passivity, synchronization, coupled reaction-diffusion neural networks, complex networks, neural networks.
Textul de pe ultima copertă
This book introduces recent results on output synchronization of complex dynamical networks with single and multiple weights. It discusses novel research ideas and a number of definitions in complex dynamical networks, such as H-Infinity output synchronization, adaptive coupling weights, multiple weights, the relationship between output strict passivity and output synchronization. Furthermore, it methodically edits the research results previously published in various flagship journals and presents them in a unified form. The book is of interest to university researchers and graduate students in engineering and mathematics who wish to study output synchronization of complex dynamical networks.
Caracteristici
Introduces the latest results on output synchronization of complex dynamical networks Considers new complex dynamical network models with multiple weights Helps readers to understand output synchronization of complex dynamical networks by discussing the theory and analyzing research results