This book intends to introduce some recent results on passivity of complex dynamical networks with single weight and multiple weights. The book collects novel research ideas and some definitions in complex dynamical networks, such as passivity, output strict passivity, input strict passivity, finite-time passivity, and multiple weights. Furthermore, the research results previously published in many flagship journals are methodically edited and presented in a unified form. The book is likely to be of interest to university researchers and graduate students in Engineering and Mathematics who wish to study the passivity of complex dynamical networks.

About the Author:

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 January 2014.

In January 2014, he joined the School of Computer Science and Technology, Tiangong University, Tianjin, China. In 2014, he was a Program Aid with Texas A & M University at Qatar, Doha, Qatar, for two months. From June 2015 to July 2015 and from July 2016 to August 2016, he was Postdoctoral Research Associate with Texas A & M University at Qatar. From June 2017 to September 2017, he was Associate Research Scientist in Texas A & M University at Qatar. He has authored two books entitled Analysis and control of coupled neural networks with reaction-diffusion terms (Springer, 2017) and Analysis and control of output synchronization for complex dynamical networks (Springer, 2018). He is currently Professor with the School of Computer Science and Technology, Tiangong University. His current research interests include passivity, synchronization, cooperative control, complex networks, coupled neural networks, coupled reaction–diffusion neural networks, and multiagent systems.

Dr. Wang currently serves as Associate Editor for the Neurocomputing and was Managing Guest Editor for the Special Issue of Dynamical behaviors of coupled neural networks with reaction-diffusion terms: analysis, control and applications in Neurocomputing.

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