Synchronization of coupled memristive inertial delayed neural networks with impulse and intermittent control

Zhang, Wei; Qi, Jiangtao

doi:10.1007/s00521-020-05540-z

Cited by 25 publications

(21 citation statements)

References 37 publications

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“…Up to date, lots of significant and worthwhile synchronization results on MNNs have been obtained [30]- [33]. But just a few results on the dynamical behaviors of CMNNs have been reported [34]- [37]. Unfortunately, the synchronization and H ∞ synchronization of CDRDMNNs have not been studied.…”

Section: Event-triggered Synchronizationmentioning

confidence: 99%

“…In [33], some adequate conditions were acquired for ensuring MNNs exponential stabilization. However, only a small number of works discussed the dynamical behaviors of coupled memristive neural networks (CMNNs) [34]- [37]. In [34], some new sufficient conditions were established to guarantee the periodicity and synchronization of CMNNs with supremums.…”

Section: Introductionmentioning

confidence: 99%

“…First, two types of CDRDMNNs models are proposed that the first type is CDRDMNNs with state coupling and the second one is CDRDMNNs with spatial diffusion coupling in virtue of an event-triggered approach. Compared with [34]- [37], the considered model includes reaction-diffusion terms in this paper, named as CDRDMNNs, which is more general and complicated in many real networks. 2.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Event-triggered Synchronizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Lin

Liu

Huang

2022

Preprint

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“…Its chaotic dynamic behaviors can be applied to secure communication. Zhang et al [10] studied synchronization problem of coupled memristive neural networks, and Cao et al [11] discussed exponential anti-synchronization problem. Besides, Liu et al [12] investigated the dynamical robustness and transition of firing modes of multilayer memristive neural networks in detail.…”

Section: Introductionmentioning

confidence: 99%

Multistable Dynamics in a Hopfield Neural Network Under Electromagnetic Radiation and Dual Bias Currents

Wan

Yan

et al. 2021

Preprint

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This paper investigates a Hopfield neural network (HNN) under the simulation of external electromagnetic radiation and dual bias currents, in which the fluctuation of magnetic flux across the neuron membrane is used to emulate the influence of electromagnetic radiation. Utilizing conventional analytical methods, the basic properties of the proposed Hopfield neural network are discussed. Due to the addition of electromagnetic radiation and dual bias currents, the Hopfield neural network shows high sensitivity to system parameters and initial conditions. The proposed Hopfield neural network possesses multistability with periodic attractor, quasi-periodic attractor, chaotic attractor and transient chaotic attractor, and all of the attractors are hidden attractors because there is no equilibrium point in the system. In particular, when the neuron membrane magnetic flux is different, the system can present transient chaos with different chaotic times. More interestingly, with the change of system parameters, the proposed Hopfield neural network can exhibit parallel bifurcation behaviors. Finally, the Multisim simulation and hardware experiment results based on discrete electronic components are conducted to support the numerical ones. These results could give useful information to the study of nonlinear dynamic characteristics of the Hopfield neural network.

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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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Synchronization of coupled memristive inertial delayed neural networks with impulse and intermittent control

Cited by 25 publications

References 37 publications

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

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

Multistable Dynamics in a Hopfield Neural Network Under Electromagnetic Radiation and Dual Bias Currents

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

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