Impulsive control for the synchronization of coupled neural networks with reaction–diffusion terms

Wei, Pu-Chong; Wang, Jinliang; Huang, Yanli; Xu, Beibei; Ren, Shun-Yan

doi:10.1016/j.neucom.2016.05.034

Cited by 31 publications

(16 citation statements)

References 44 publications

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“…Up to now, plenty of impressive studies on the synchronization of CRDNNs have been achieved. e authors investigated the impulsive synchronization [9], the adaptive synchronization [10], and the passivity [11] of CRDNNs. e abovementioned works assumed that the global spatial state information was known, and all these research studies aimed to synchronize all states.…”

Section: Introductionmentioning

confidence: 99%

Distributed Event‐Triggered Output Synchronization of Complex‐Valued Memristive Reaction‐Diffusion Complex Networks with Spatial Sampled‐Data

Chen

Cheng

2021

Complexity

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This study addresses the problem of output quasisynchronization for coupled complex-valued memristive reaction-diffusion complex networks via the distributed event-triggered control scheme. First, by using the separate method, set value mapping, and intermediate value theorem, the complex-valued memristive reaction-diffusion complex networks can be transferred into two semi-uncertain real-valued reaction-diffusion complex networks. Second, a distributed output piecewise event-triggered control (OPETC) scheme with spatial sampled-data is first proposed including a spatial sampling event-triggered generator and spatiotemporal sampling state feedback controller. Furthermore, this scheme can effectively save the measurement resources and lower the update rate of controllers in spatial and time domain. Third, the synchronization analysis is considered by utilizing an appropriate Lyapunov function, the Halanay inequality, and the improved Wirtinger inequality. Subsequently, several output event-triggered quasisynchronization criteria are derived. The relations among event trigger conditions, spatial sampling interval, convergence rate, and control gain are given by rigorous mathematical derivation. Finally, multiple simulations are compared to substantiate the validation of the OPETC scheme.

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Section: Introductionmentioning

confidence: 99%

Distributed Event‐Triggered Output Synchronization of Complex‐Valued Memristive Reaction‐Diffusion Complex Networks with Spatial Sampled‐Data

Chen

Cheng

2021

Complexity

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“…Xie et al [30] investigated the synchronization of time-varying delays coupled reaction-diffusion neural networks with pinning impulsive control. Wei et al [31] realized synchronization of the coupled reaction-diffusion neural networks with timevarying delay by impulsive control. Li et al [32] investigated the master-slave exponential synchronization of the neural networks with time-varying delays via discontinuous impulsive control.…”

Section: Introductionmentioning

confidence: 99%

Synchronization of Time Delay Coupled Neural Networks Based on Impulsive Control

Fang

Yin

et al. 2020

Mathematical Problems in Engineering

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This paper investigates the impulsive synchronization of time delay coupled neural networks. Based on the Lyapunov stability theory and impulsive control method, a distributed delayed impulsive controller is designed to realize synchronization of the coupled neural networks. A new impulsive delayed inequality is proposed, where the control effect of distributed delayed impulses is fully considered. In addition, a suitable Lyapunov-like function is established to prove the stability of the synchronization system. Numerical simulation examples are introduced to illustrate the effectiveness and feasibility of the main results.

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“…In the latest decades, the synchronization issue of coupled neural networks has been extensively investigated (Chen et al, 2015; He et al, 2017; Hu et al, 2010; Li X et al, 2017; Lu et al, 2016; Peng et al, 2017; Senan et al, 2017; Sheng and Zeng, 2017; Tang et al, 2017; Wei et al, 2016; Yang et al, 2016; Yu et al, 2016; Zhang and Sun, 2009). The synchronization of coupled neural networks with reaction-diffusion effects was considered in Hu et al (2010) and Wei et al (2016), and some useful criteria dependent on the diffusion coefficients were obtained. The periodically intermittent control (Yang et al, 2016) was used to study the synchronization and exponential stabilization of fuzzy memristive neural networks.…”

Section: Introductionmentioning

confidence: 99%

Synchronization of delayed neural networks with hybrid coupling via impulsive control

Wang

Cao

2019

Transactions of the Institute of Measurement and Control

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The synchronization problem of coupled neural networks via impulsive control is investigated in the present paper. Based on a time varying Lyapunov functional associated with the impulsive time sequence, the delay-dependent criteria in terms of linear matrix inequalities are derived to guarantee the synchronization of the coupled neural networks. The obtained criteria are closely related to both the lower and the upper bound of the adjacent impulsive instant difference. By solving the corresponding linear matrix inequalities, the synchronization criteria can be used to estimate the upper bound of both transmission delay and distributed-delay. The low-dimensional criteria also are obtained for the coupled neural networks with identical nodes. Finally, two examples are given to illustrate the validity of the proposed hybrid control.

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Impulsive control for the synchronization of coupled neural networks with reaction–diffusion terms

Cited by 31 publications

References 44 publications

Distributed Event‐Triggered Output Synchronization of Complex‐Valued Memristive Reaction‐Diffusion Complex Networks with Spatial Sampled‐Data

Distributed Event‐Triggered Output Synchronization of Complex‐Valued Memristive Reaction‐Diffusion Complex Networks with Spatial Sampled‐Data

Synchronization of Time Delay Coupled Neural Networks Based on Impulsive Control

Synchronization of delayed neural networks with hybrid coupling via impulsive control

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