Impulsive synchronization schemes of stochastic complex networks with switching topology: Average time approach

Li, Chaojie; Yu, Wenwu; Huang, Tingwen

doi:10.1016/j.neunet.2014.02.013

Cited by 143 publications

(64 citation statements)

References 34 publications

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“…Uncertainty can be frequently found in the topology structures [16] and in system dynamics as well. For most of previous works about synchronization of complex networks [17][18][19], it is assumed that the system parameters were completely known. To reflect practical situations, uncertainties should be considered in complex networks.…”

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confidence: 99%

Topology and parameters recognition of uncertain complex networks via nonidentical adaptive synchronization

2016

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Topology plays an essential role in chaotic behaviors and evolution performances of a complex dynamical network. In this paper, recognition issue for unknown system parameters and topology of uncertain general complex dynamical networks with nonlinear couplings and time-varying delay is investigated through generalized outer synchronization. Firstly, the unknown system parameters and topology in master network are successfully estimated by a slave network. Secondly, the unknown system parameters of both two networks and the unknown topology of the master network are effectively evaluated in view of generalized outer synchronization based on an adaptive feedback control strategy. Two situations of parameters and topologies recognition are efficiently verified by illustrative numerical simulations.

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confidence: 99%

Topology and parameters recognition of uncertain complex networks via nonidentical adaptive synchronization

2016

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“…A new susceptible-infected-susceptible (SIS) epidemic model incorporated with multistage infection (infection delay) and an infective medium (propagation vector) over complex networks [3][4][5][6]. In [7][8][9], a novel ISIR epidemic model with nonlinear forces of infection to characterize the epidemic spread on social contact networks with the consideration of the "crowding" or "protection effect" is proposed. For this class of dynamic networks, [10][11][12][13][14] derive an epidemic threshold, considering the susceptible-infected-susceptible epidemic model.…”

Section: Background and Statusmentioning

confidence: 99%

“…A simplified version of the epidemic threshold is proposed for undirected networks. References [8,9,15] investigated the mathematical epidemic model, SEIR (SusceptibleExposed-Infected-Removed), through extensive simulations of the effects of social network on epidemic spread in a Small World (SW) network, to understand how an influenza epidemic spreads through a human population. A combined SEIR-SW model was built, to help understand the dynamics of infectious disease in a community and to identify the main characteristics of epidemic transmission and its evolution over time.…”

Section: Background and Statusmentioning

confidence: 99%

Online Social Network Emergency Public Event Information Propagation and Nonlinear Mathematical Modeling

Liu¹,

Liu²,

Zeng³

2017

Complexity

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Emergency public event arises everyday on social network. The information propagation of emergency public event (favorable and harmful) is researched. The dynamics of a susceptible-infected-susceptible and susceptible-infected-removed epidemic models incorporated with information propagation of emergency public event are studied. In particular, we investigate the propagation model and the infection spreading pattern using nonlinear dynamic method and results obtained through extensive numerical simulations. We further generalize the model for any arbitrary number of infective network nodes to mimic existing scenarios in online social network. The simulation results reveal that the inclusion of multiple infective node achieved stability and equilibrium in the proposed information propagation model.

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“…The research of neural networks has received intensive attention due to their wide applications in classification of pattern recognition, static image processing, signal processing, optimization problems, mechanics of structures and materials, smart antenna arrays and other scientific areas during the past few decades [1][2][3][4][5][6][7][8][9][10][11][12][13]. In the hardware implementation of neural networks, time delays in particular time-varying delays are unavoidably encountered in the signal transmission among the neurons due to the finite speed of switching and transmitting signals, which may cause undesirable dynamical behaviors such as instability and oscillation [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Finite-time stabilization of uncertain neural networks with distributed time-varying delays

Yang

Huang

2016

Neural Comput & Applic

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In this paper, the problem of finite-time stabilization for a class of uncertain neural networks with distributed time-varying delays is investigated. Based on the Lyapunov stability theory and integral inequality technique, some sufficient LMI conditions are derived to ensure the finite-time stability of considered neural networks. In addition, the upper bound of the settling time for stabilization is estimated. Numerical simulations are carried out to demonstrate the effectiveness of the obtained results.

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Impulsive synchronization schemes of stochastic complex networks with switching topology: Average time approach

Cited by 143 publications

References 34 publications

Topology and parameters recognition of uncertain complex networks via nonidentical adaptive synchronization

Topology and parameters recognition of uncertain complex networks via nonidentical adaptive synchronization

Online Social Network Emergency Public Event Information Propagation and Nonlinear Mathematical Modeling

Finite-time stabilization of uncertain neural networks with distributed time-varying delays

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