Synchronization for stochastic coupled networks with Lévy noise via event-triggered control

Dong, Hailing; Luo, Ming; Xiao, Mingqing

doi:10.1016/j.neunet.2021.03.028

Cited by 10 publications

(2 citation statements)

References 41 publications

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“…Therefore, these factors have aroused great attention to the NNs with complex structures. But, only a few literatures 22,28 have been published so far on the issue of the ETC for stochastic coupled systems, which promotes the research work of this article.…”

Section: F I G U R Ementioning

confidence: 99%

Exponential synchronization of stochastic coupled neural networks with Markovian switching via event‐triggered control

Zhang

Chen

Tong

et al. 2022

Intl J Robust & Nonlinear

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This article reports on exponential synchronization for stochastic coupled neural networks (NNs) with mixed time‐varying delays, stochastic coupling strength, and Markovian switching. In order to reduce the amount of data transmission and save network resources, an event‐triggered control method is provided in this study. When the triggered condition can be met, the data can be transmitted so that the master and slave systems with limited resources and bandwidth can realize synchronization. By the Lyapunov stability theory and several analysis skills of matrix properties, some new criteria are obtained to make sure that stochastic coupled NNs are mean square exponential stability. These criteria are provided by linear matrix inequalities. Finally, a numerical case further demonstrates the validity of the proposed criteria.

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Section: F I G U R Ementioning

confidence: 99%

Exponential synchronization of stochastic coupled neural networks with Markovian switching via event‐triggered control

Zhang

Chen

Tong

et al. 2022

Intl J Robust & Nonlinear

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“…To make the network reach synchronization, many effective control methods have been proposed, such as pinning control, [39] intermittent control, [40] and event-triggered control. [41] However, there are few studies on quantitative control at present. As is well known, the signal transmission between network nodes depends on the transmission channel and channel bandwidth.…”

Section: Introductionmentioning

confidence: 99%

Finite-time synchronization of uncertain fractional-order multi-weighted complex networks with external disturbances via adaptive quantized control

2022

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In this paper, finite-time synchronization of fractional-order multi-weighted complex networks (FMCNs) with uncertain parameters and external disturbances is studied. Firstly, based on fractional calculus characteristics and Lyapunov stability theory, quantized controllers are designed to guarantee that FMCNs can achieve synchronization in a limited time with and without coupling delay respectively. Then, appropriate parameter update laws are obtained to identify uncertain parameters in FMCNs. Finally, numerical simulation examples are given to validate the correctness of the theoretical results.

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Synchronization of Stochastic Multi-Weights Complex Networks with Lévy Noise and Markovian Switching

Hong

Ren

Yang

2022

Neural Process Lett

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Synchronization for stochastic coupled networks with Lévy noise via event-triggered control

Cited by 10 publications

References 41 publications

Exponential synchronization of stochastic coupled neural networks with Markovian switching via event‐triggered control

Exponential synchronization of stochastic coupled neural networks with Markovian switching via event‐triggered control

Finite-time synchronization of uncertain fractional-order multi-weighted complex networks with external disturbances via adaptive quantized control

Synchronization of Stochastic Multi-Weights Complex Networks with Lévy Noise and Markovian Switching

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