Multiple <i>μ</i>-Stable Synchronization Control for Coupled Memristive Neural Networks With Unbounded Time Delays

Peng, Libiao; Bi, Dongjie; Xie, Xuan; Xie, Yongle

doi:10.1109/tsmc.2020.3011120

Cited by 13 publications

(3 citation statements)

References 59 publications

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“…For this reason, many scholars have been trying to use memristors to simulate neurons to achieve brain-like computation, which leads to the development of memristive neural networks (MNNs) [12,13]. An increasing number of scholars have recently investigated the dynamical behaviors of coupled MNNs (CMNNs) [14][15][16]. In [14], a distributed impulsive control strategy is employed to explore the multisynchronization problem of CMNNs.…”

Section: Introductionmentioning

confidence: 99%

“…An increasing number of scholars have recently investigated the dynamical behaviors of coupled MNNs (CMNNs) [14][15][16]. In [14], a distributed impulsive control strategy is employed to explore the multisynchronization problem of CMNNs. As discussed in [15], several synchronization conditions in fnite-time and fxed-time for CMNNs were derived.…”

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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“…Since the existence of memristor was confirmed by the HP laboratory in 2008 (Strukov et al, 2008), many scholars introduced memristor in the study of neural networks, which can more truly simulate the memory characteristics of biological neural networks. Guo and Gao (2014), Wang et al (2016), Chen et al (2018), Qi (2021), andPeng et al (2020) considered the asymptotic time synchronization of CMNN and gave full consideration to various cases of random disturbances. Various results about exponential synchronization of CMNN (Wang and Shen, 2014;Bao et al, 2016;Feng et al, 2016;Guo et al, 2018;Chen et al, 2021) were given, and Guo et al (2018) also considered multiple coupled terms.…”

Section: Introductionmentioning

confidence: 99%

Predefined-Time Stability/Synchronization of Coupled Memristive Neural Networks With Multi-Links and Application in Secure Communication

et al. 2021

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This paper explores the realization of a predefined-time synchronization problem for coupled memristive neural networks with multi-links (MCMNN) via nonlinear control. Several effective conditions are obtained to achieve the predefined-time synchronization of MCMNN based on the controller and Lyapunov function. Moreover, the settling time can be tunable based on a parameter designed by the controller, which is more flexible than fixed-time synchronization. Then based on the predefined-time stability criterion and the tunable settling time, we propose a secure communication scheme. This scheme can determine security of communication in the aspect of encrypting the plaintext signal with the participation of multi-links topology and coupled form. Meanwhile, the plaintext signals can be recovered well according to the given new predefined-time stability theorem. Finally, numerical simulations are given to verify the effectiveness of the obtained theoretical results and the feasibility of the secure communication scheme.

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Exponential Lagrangian stability and stabilization of memristor-based neural networks with unbounded time-varying delays

2022

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Multiple μ-Stable Synchronization Control for Coupled Memristive Neural Networks With Unbounded Time Delays

Cited by 13 publications

References 59 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

Predefined-Time Stability/Synchronization of Coupled Memristive Neural Networks With Multi-Links and Application in Secure Communication

Exponential Lagrangian stability and stabilization of memristor-based neural networks with unbounded time-varying delays

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