Pinning control for passivity and synchronization of coupled memristive reaction–diffusion neural networks with time-varying delay

Yue, Chen-Xu; Wang, Lidan; Hu, Xiaofang; Tang, Hong-An; Duan, Shukai

doi:10.1016/j.neucom.2019.09.103

Cited by 20 publications

(14 citation statements)

References 43 publications

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“…Diferent from other control methods, pinning control is a very efective control scheme in which only a small fraction of nodes are chosen to be controlled. So far, some scholars have investigated pinning synchronization control problem of the coupled delayed NNs (CDNNs) [31] and CMNNs [32]. In [31], several criteria were proposed to ensure the cluster synchronization of nonlinear CDNNs in both fnitetime and fxed-time aspects based on the pinning control strategy.…”

Section: Introductionmentioning

confidence: 99%

“…In [31], several criteria were proposed to ensure the cluster synchronization of nonlinear CDNNs in both fnitetime and fxed-time aspects based on the pinning control strategy. In a previous study [32], Yue et al addressed the passivity and synchronization of CMNNs by making use of two efective pinning control strategies. However, the investigation on pinning antisynchronization of MWCNNs is very rare [33].…”

Section: Introductionmentioning

confidence: 99%

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Antisynchronization and Generalized Pinning Control of Multiweighted Coupled Complex-Valued Delayed Memristive Neural Networks

Huang

2023

Discrete Dynamics in Nature and Society

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In this article, antisynchronization problem of multiweighted coupled complex-valued delayed memristive neural networks (MWCCVDMNNs) with and without coupling delays are investigated. First, via devising a suitable controller and constructing an appropriate Lyapunov functional, a criterion for ensuring antisynchronization of MWCCVDMNNs is derived. Second, we research the generalized pinning antisynchronization of MWCCVDMNNs by creating a generalized pinning controller to guarantee that the considered networks can accomplish antisynchronization. Similarly, several sufficient conditions guaranteeing the antisynchronization and generalized pinning antisynchronization of MWCCVDMNNs with coupling delays are also presented. Third, two numerical examples are provided to verify the correctness of the obtained antisynchronization results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Antisynchronization and Generalized Pinning Control of Multiweighted Coupled Complex-Valued Delayed Memristive Neural Networks

Huang

2023

Discrete Dynamics in Nature and Society

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“…Compared with the traditional resistor, the past dynamic history of memristor can be remembered since the number of this memristor relies on the quantity of charge running through here. Thus, there are wide applications referred to the memristor in many domains involving image processing, signal processing and optimization [26]- [28]. Moreover, by means of replacing resistor by the memristor, memristive neural networks (MNNs) can show the neural transmission in the brain primely [29].…”

Section: Introductionmentioning

confidence: 99%

Event-triggered synchronization and H∞ synchronization of coupled delayed reaction-diffusion memristive neural networks

Lin

Liu

Huang

2022

Preprint

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This paper settles event-triggered synchronization and H∞ synchronization matters for two types of coupled delayed reaction-diffusion memristive neural networks (CDRDMNNs). First of all, several synchronization and H∞ synchronization conditions are acquired for CDRDMNNs with state coupling in virtue of exploiting Lyapunov stability theory in combination with proper controllers of the triggering event. Then, for CDRDMNNs with spatial diffusion coupling, event-triggered synchronization and H∞ synchronization are investigated as well. Finally, the correctness of the deduced synchronization and H∞synchronization results is verified by two given numerical examples.

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“…Consequently, it is interesting to research the passivity for coupled MNNs (CMNNs) [21]. In [21], Yue et al investigated the passivity of delayed CMNNs with reactiondiffusion terms with the aid of two pinning control schemes and some inequality techniques.…”

Section: Introductionmentioning

confidence: 99%

Feedback Control for Passivity of Memristor‐Based Multiple Weighted Coupled Neural Networks

Wang

Tang

Xia

et al. 2022

Discrete Dynamics in Nature and Society

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This paper investigates the passivity of multiple weighted coupled memristive neural networks (MWCMNNs) based on the feedback control. Firstly, a kind of memristor-based coupled neural network model with multiple weights is presented for the first time. Furthermore, a novel passivity criterion for MWCMNNs is established by constructing an appropriate Lyapunov functional and developing a suitable feedback controller. In addition, with the assistance of some inequality techniques, sufficient conditions for ensuring the input strict passivity and output strict passivity of MWCMNNs are derived. Finally, the validity of the theoretical results is verified by a numerical example.

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Pinning control for passivity and synchronization of coupled memristive reaction–diffusion neural networks with time-varying delay

Cited by 20 publications

References 43 publications

Antisynchronization and Generalized Pinning Control of Multiweighted Coupled Complex-Valued Delayed Memristive Neural Networks

Antisynchronization and Generalized Pinning Control of Multiweighted Coupled Complex-Valued Delayed Memristive Neural Networks

Event-triggered synchronization and H∞ synchronization of coupled delayed reaction-diffusion memristive neural networks

Feedback Control for Passivity of Memristor‐Based Multiple Weighted Coupled Neural Networks

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