Analysis And Control Of Output Synchronization For Complex Dynamical Networks


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Analysis and Control of Output Synchronization for Complex Dynamical Networks


Analysis and Control of Output Synchronization for Complex Dynamical Networks

Author: Jin-Liang Wang

language: en

Publisher: Springer

Release Date: 2018-08-14


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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.

Dynamical Behaviors of Multiweighted Complex Network Systems


Dynamical Behaviors of Multiweighted Complex Network Systems

Author: Jin-Liang Wang

language: en

Publisher: John Wiley & Sons

Release Date: 2024-11-25


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Highly comprehensive resource for studying neural networks, complex networks, synchronization, passivity, and associated applications Dynamical Behaviors of Multiweighted Complex Network Systems discusses the dynamical behaviors of various multiweighted complex dynamical networks, with detailed insight on synchronization for directed and undirected complex networks (CNs) with multiple state or delayed state couplings subject to recoverable attacks, along with passivity and synchronization for coupled neural networks with multi-weights (CNNMWs) by virtue of devised proportional-integral-derivative (PID) controllers. The book also investigates finite-time synchronization (FTS) and H-infinity synchronization for two types of coupled neural networks (CNNs) and focuses on finite-time passivity (FTP) and finite-time synchronization (FTS) for complex dynamical networks with multiple state/derivative couplings based on the proportional-derivative (PD) control method. Final chapters consider finite-time output synchronization and H-infinity output synchronization problems, and multiple weighted coupled reaction-diffusion neural networks (CRDNNs) with and without coupling delays. Other topics covered in Dynamical Behaviors of Multiweighted Complex Network Systems include: Criteria of FTP for complex dynamical networks with multiple state couplings (CDNMSCs), formulated by utilizing the PD controller Finite-time passivity (FTP) concepts for the spatially and temporally systems with different dimensions of output and input FTS and finite time H-infinity synchronization problems for CDNs with multiple state/derivative couplings by utilizing state feedback control approach and selecting suitable parameter adjustment schemes Adaptive output synchronization and output synchronization of CDNs with multiple output or output derivative couplings, and other adaptive control schemes Enabling readers to understand foundational concepts and grasp the latest research, Dynamical Behaviors of Multiweighted Complex Network Systems is essential for all who study neural networks, complex networks, synchronization, passivity, and their applications.

Analysis and Control of Coupled Neural Networks with Reaction-Diffusion Terms


Analysis and Control of Coupled Neural Networks with Reaction-Diffusion Terms

Author: Jin-Liang Wang

language: en

Publisher: Springer

Release Date: 2017-06-07


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This book introduces selected recent findings on the analysis and control of dynamical behaviors for coupled reaction-diffusion neural networks. It presents novel research ideas and essential definitions concerning coupled reaction-diffusion neural networks, such as passivity, adaptive coupling, spatial diffusion coupling, and the relationship between synchronization and output strict passivity. Further, it gathers research results previously published in many flagship journals, presenting them in a unified form. As such, the book will be of interest to all university researchers and graduate students in Engineering and Mathematics who wish to study the dynamical behaviors of coupled reaction-diffusion neural networks.