Out Lag Synchronization of Fractional Order Delayed Complex Networks with Coupling Delay via Pinning Control

Zhang, Wei Wei; Cao, Jinde; Chen, Dingyuan; Alsaedi, Ahmed

doi:10.1155/2019/5612150

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…Moreover, dynamic behaviors of fractional-order systems have attracted the attention of many researchers because of their practical applications. In the past decade, the dynamic analysis of the fractional-order systems has achieved many outstanding results [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

New Results on Synchronization of Fractional-Order Memristor‐Based Neural Networks via State Feedback Control

Liu

et al. 2020

Complexity

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This paper addresses the synchronization issue for the drive-response fractional-order memristor‐based neural networks (FOMNNs) via state feedback control. To achieve the synchronization for considered drive-response FOMNNs, two feedback controllers are introduced. Then, by adopting nonsmooth analysis, fractional Lyapunov’s direct method, Young inequality, and fractional-order differential inclusions, several algebraic sufficient criteria are obtained for guaranteeing the synchronization of the drive-response FOMNNs. Lastly, for illustrating the effectiveness of the obtained theoretical results, an example is given.

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Section: Introductionmentioning

confidence: 99%

New Results on Synchronization of Fractional-Order Memristor‐Based Neural Networks via State Feedback Control

Liu

et al. 2020

Complexity

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“…Some works have investigated the synchronization of the complex dynamical networks via control strategies in recent years [15][16][17][18]. For example, in [19], the problem of synchronization control of complex dynamical networks subject to nonlinear couplings and uncertainties has been investigated. In [20], the pinning impulsive synchronization problem for a class of complex dynamical networks with time-varying delay has been summarized.…”

Section: Introductionmentioning

confidence: 99%

Synchronization of Complex Dynamical Networks with Hybrid Time Delay under Event‐Triggered Control: The Threshold Function Method

2019

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This paper investigates the synchronization of general complex dynamical networks (CDNs) with both internal delay and transmission delay. Event-triggered mechanism is applied for the feedback controllers, in which the triggered function is formed as a nonincreasing function. Both continuous feedback and sampled-data feedback methods are studied. According to Lyapunov stability theorem and generalized Halanay’s inequality, quasi-synchronization criteria are derived at first. The synchronization error is bounded with some parameters of the triggered function. Then, the completed synchronization can be guaranteed as a special case. Finally, coupled neural networks as numerical simulation examples are given to verify the theoretical results.

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“…Ibrahim and Jung developed an adaptive control methodology to guarantee synchronization between a bidirectional ring-structured FHN neuronal network under the effect of external electrical stimulation with single-and dual-state gap junctions 1 . Zhang investigated the out-lag synchronization by proposing a pinning control scheme in fractional-order complex networks with coupling and internal delays 59 . Petkoski and Jirsa studied how the phase lag synchronization between the neurons of different brain regions is governed by the spatio-temporal organization of the brain by using self-sustained time-delayed chaotic oscillators 60 .…”

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confidence: 99%

Lag synchronization of coupled time-delayed FitzHugh–Nagumo neural networks via feedback control

Ibrahim

Kamran

Mannan

et al. 2021

Sci Rep

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Synchronization plays a significant role in information transfer and decision-making by neurons and brain neural networks. The development of control strategies for synchronizing a network of chaotic neurons with time delays, different direction-dependent coupling (unidirectional and bidirectional), and noise, particularly under external disturbances, is an essential and very challenging task. Researchers have extensively studied the synchronization mechanism of two coupled time-delayed neurons with bidirectional coupling and without incorporating the effect of noise, but not for time-delayed neural networks. To overcome these limitations, this study investigates the synchronization problem in a network of coupled FitzHugh–Nagumo (FHN) neurons by incorporating time delays, different direction-dependent coupling (unidirectional and bidirectional), noise, and ionic and external disturbances in the mathematical models. More specifically, this study investigates the synchronization of time-delayed unidirectional and bidirectional ring-structured FHN neuronal systems with and without external noise. Different gap junctions and delay parameters are used to incorporate time-delay dynamics in both neuronal networks. We also investigate the influence of the time delays between connected neurons on synchronization conditions. Further, to ensure the synchronization of the time-delayed FHN neuronal networks, different adaptive control laws are proposed for both unidirectional and bidirectional neuronal networks. In addition, necessary and sufficient conditions to achieve synchronization are provided by employing the Lyapunov stability theory. The results of numerical simulations conducted for different-sized multiple networks of time-delayed FHN neurons verify the effectiveness of the proposed adaptive control schemes.

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Out Lag Synchronization of Fractional Order Delayed Complex Networks with Coupling Delay via Pinning Control

Cited by 6 publications

References 34 publications

New Results on Synchronization of Fractional-Order Memristor‐Based Neural Networks via State Feedback Control

New Results on Synchronization of Fractional-Order Memristor‐Based Neural Networks via State Feedback Control

Synchronization of Complex Dynamical Networks with Hybrid Time Delay under Event‐Triggered Control: The Threshold Function Method

Lag synchronization of coupled time-delayed FitzHugh–Nagumo neural networks via feedback control

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