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Title: Global cluster synchronization in nonlinearly coupled community networks with heterogeneous coupling delays
Authors: Tseng, Jui-Pin
Contributors: 應用數學系
Keywords: Differential equations;Neural networks;Synchronization;Time delay;Cluster synchronization;Coupled systems;Delay;Non-identical nodes;Nonlinear coupling;Complex networks;artificial neural network;cluster analysis;computer simulation;nonlinear system;time factor;trends;Cluster Analysis;Computer Simulation;Neural Networks (Computer);Nonlinear Dynamics;Time Factors
Date: 2017-02
Issue Date: 2017-07-20 16:55:59 (UTC+8)
Abstract: This investigation establishes the global cluster synchronization of complex networks with a community structure based on an iterative approach. The units comprising the network are described by differential equations, and can be non-autonomous and involve time delays. In addition, units in the different communities can be governed by different equations. The coupling configuration of the network is rather general. The coupling terms can be non-diffusive, nonlinear, asymmetric, and with heterogeneous coupling delays. Based on this approach, both delay-dependent and delay-independent criteria for global cluster synchronization are derived. We implement the present approach for a nonlinearly coupled neural network with heterogeneous coupling delays. Two numerical examples are given to show that neural networks can behave in a variety of new collective ways under the synchronization criteria. These examples also demonstrate that neural networks remain synchronized in spite of coupling delays between neurons across different communities; however, they may lose synchrony if the coupling delays between the neurons within the same community are too large, such that the synchronization criteria are violated.
Relation: Neural Networks, 86, 18-31
Data Type: article
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