Exponential synchronization of complex dynamical networks with time-varying inner coupling via event-triggered communication

Hao, Dingjun; Chen, Weisheng; Jia, Jinping; Liu, Jiayun; Zhang, Zhengqiang

doi:10.1016/j.neucom.2017.03.035

Cited by 37 publications

(10 citation statements)

References 41 publications

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“…It can conquer the shortcomings of the continuous control method and reduce the unnecessary transmission of the communication media during the process of information exchanging. In recent years, some interesting results on eventtriggered passivity and synchronization of complex networks have been gained [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. The authors in [16] designed two kinds of distributed event-triggering laws and some criteria that insure the event-based exponential synchronization were established.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered communication for passivity and synchronisation of multi‐weighted coupled neural networks with and without parameter uncertainties

Wang

Huang

Yang

2020

IET Control Theory & Applications

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A multi-weighted coupled neural networks (MWCNNs) model with event-triggered communication is studied in this paper. On the one hand, the passivity of the presented network model is studied by utilizing Lyapunov stability theory and some inequality techniques, and a synchronization criterion based on the obtained output-strict passivity condition of MWCNNs with event-triggered communication is derived. On the other hand, some robust passivity and robust synchronization criteria based on output-strict passivity of the proposed network with uncertain parameters are presented. At last, two numerical examples are provided to testify the effectiveness of the output-strict passivity and robust synchronization results.

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Section: Introductionmentioning

confidence: 99%

Event‐triggered communication for passivity and synchronisation of multi‐weighted coupled neural networks with and without parameter uncertainties

Wang

Huang

Yang

2020

IET Control Theory & Applications

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“…The researchers proposed the positioning of weight centers of mass, which improved the positioning accuracy to some extent. Juan J. Galvez, Patrick Carroll,et al [14][15][16][17][18][19][20] proposed a novel probabilistic graphical model called Bayesian Network based Program Dependence Graph (BNPDG) that has the excellent inference capability across nonadjacent WSN nodes. They focused on applying the BNPDG at indoor node localization.…”

Section: Background and Researchmentioning

confidence: 99%

Wireless Sensor Network Dynamic Mathematics Modeling and Node Localization

Liu

2018

Wireless Communications and Mobile Computing

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With the rapid development of wireless sensor network (WSN) technology and its localization method, localization is one of the basic services for data collection in WSN. The localization accuracy often depends on the accuracy of distance estimation. Because of the constraint in size, power, and cost of sensor nodes, the investigation of efficient location algorithms which satisfy the basic accuracy requirement for WSN meets new challenges. This paper proposes a novel intelligent node localization algorithm in WSN based on beacon nodes to improve the precision in location estimation. Firstly, system model of WSN node localization is constructed according to the WSN environment. Then traditional WSN node localization methods such as DV-HOP, GA, and PSO are studied. Localization algorithm of WSN is proposed by using dynamic mathematics modeling. And the result of simulation, which is compared to the traditional algorithm, indicated that this algorithm is better to improve the accuracy and coverage of WSN. The simulation results show that the performance of the proposed WSN location algorithm is better than the traditional localization algorithms.

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“…Recently, significant effort has been devoted to the study of the synchronization of complex dynamical networks [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. However, the synchronization criteria in most of the existing studies contain unknown parameters, thereby making system synchronization difficult for workers to verify.…”

Section: Global Exponential Synchronization Of System (1) Using the Hmentioning

confidence: 99%

“…Researchers have proposed several effective control methods, and significant conclusions have been obtained [18][19][20][21][22][23][24][25]. Guaranteed cost synchronization for complex networks was addressed 2 Complexity in [18] by designing a dynamic feedback controller with guaranteed cost synchronization.…”

Section: Introductionmentioning

confidence: 99%

Global Exponential Synchronization of Nonlinearly Coupled Complex Dynamical Networks with Time-Varying Coupling Delays

Luo

Shu

Zhou³

2017

Complexity

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This paper focuses on the global exponential synchronization problem of nonlinearly coupled complex dynamical networks with time-varying coupling delays. Several simple and generic global exponential synchronization criteria are derived based on the Lyapunov stability theory and the Dini derivatives using the Halanay and generalized Halanay inequalities. These criteria rely on system parameters alone and can be used conveniently in practical applications. In addition, the system parameters do not satisfy the conditions of the proposed criteria. That is, the system itself cannot synchronize. However, system synchronization can be achieved by adding the appropriate feedback controllers, thereby providing a practical and effective control method for complex dynamical networks. An estimation method of exponential convergence rate is also presented. Finally, the effectiveness of the proposed criteria is verified through numerical simulations.

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Exponential synchronization of complex dynamical networks with time-varying inner coupling via event-triggered communication

Cited by 37 publications

References 41 publications

Event‐triggered communication for passivity and synchronisation of multi‐weighted coupled neural networks with and without parameter uncertainties

Event‐triggered communication for passivity and synchronisation of multi‐weighted coupled neural networks with and without parameter uncertainties

Wireless Sensor Network Dynamic Mathematics Modeling and Node Localization

Global Exponential Synchronization of Nonlinearly Coupled Complex Dynamical Networks with Time-Varying Coupling Delays

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