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Journal ArticleDOI

Synchronization of delayed complex dynamical networks with impulsive and stochastic effects

TLDR
In this article, the globally exponential synchronization of delayed complex dynamical networks with impulsive and stochastic perturbations is studied and the concept named average impulsive interval with elasticity number of impulsive sequence is introduced to get a less conservative synchronization criterion.
Abstract
In this paper, the globally exponential synchronization of delayed complex dynamical networks with impulsive and stochastic perturbations is studied The concept named “average impulsive interval” with “elasticity number” of impulsive sequence is introduced to get a less conservative synchronization criterion By comparing with existing results, in which maximum or minimum of impulsive intervals are used to derive the synchronization criterion, the proposed synchronization criterion increases (or decreases) the impulse distances, which leads to the reduction of the control cost (or enhance the robustness of anti-interference) as the most important characteristic of impulsive synchronization techniques It is discovered in our criterion that “elasticity number” has influence on synchronization of delayed complex dynamical networks but has no influence on that of non-delayed complex dynamical networks Numerical simulations including a small-world network coupled with delayed Chua’s circuit are given to show the effectiveness and less conservativeness of the theoretical results

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Citations
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Journal ArticleDOI

Synchronization Control for Nonlinear Stochastic Dynamical Networks: Pinning Impulsive Strategy

TL;DR: Numerical simulations are exploited to demonstrate the effectiveness of the pinning impulsive strategy proposed, which guarantees that the whole state-coupled dynamical network can be forced to some desired trajectory by placing impulsive controllers on a small fraction of nodes.
Journal ArticleDOI

Synchronization of Coupled Reaction-Diffusion Neural Networks with Time-Varying Delays via Pinning-Impulsive Controller

TL;DR: Global exponential synchronization stability in an array of linearly diffusively coupled reaction-diffusion neural networks with time-varying delays is investigated by adding impulsive controller to a small fraction of nodes (pinning-impulsive controller).
Journal ArticleDOI

Fixed-Time Synchronization of Complex Networks With Impulsive Effects via Nonchattering Control

TL;DR: This paper proposes a new Lyapunov function, which is continuous and does not include any sign function, and hence, the chattering phenomenon in most of the existing results is overcome and an optimal algorithm is proposed for the estimation of the settling time.
Journal ArticleDOI

Finite-Time Synchronization of Coupled Networks With Markovian Topology and Impulsive Effects

TL;DR: It is demonstrated theoretically and numerically that the number of consecutive impulses with minimum impulsive interval of the desynchronizing impulsive sequence should not be too large.
Journal ArticleDOI

Synchronization of Randomly Coupled Neural Networks With Markovian Jumping and Time-Delay

TL;DR: By designing a novel Lyapunov functional, using some inequalities and the properties of random variables, several delay-dependent synchronization criteria are derived for the coupled networks of continuous-time version and its discrete-time analogues.
References
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Journal ArticleDOI

Collective dynamics of small-world networks

TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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Matrix Analysis

TL;DR: In this article, the authors present results of both classic and recent matrix analyses using canonical forms as a unifying theme, and demonstrate their importance in a variety of applications, such as linear algebra and matrix theory.
Journal ArticleDOI

Epidemic Spreading in Scale-Free Networks

TL;DR: A dynamical model for the spreading of infections on scale-free networks is defined, finding the absence of an epidemic threshold and its associated critical behavior and this new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.

Evolution of the social network of scientific collaborations

TL;DR: The results indicate that the co-authorship network of scientists is scale-free, and that the network evolution is governed by preferential attachment, affecting both internal and external links, and a simple model is proposed that captures the network's time evolution.
Journal ArticleDOI

Evolution of the social network of scientific collaborations

TL;DR: In this paper, the authors analyzed the evolution of the co-authorship network of scientists and found that the network is scale-free and the network evolution is governed by preferential attachment, a8ecting both internal and external links.
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