Fixed-time synchronization of coupled memristor-based neural networks with time-varying delays

Yang, Chao; Huang, Lihong; Cai, Zhiping

doi:10.1016/j.neunet.2019.04.008

Cited by 54 publications

(23 citation statements)

References 36 publications

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“…In additon, In [17]- [19] fixed-time synchronization problems of complex networks are dealt with. References [5], [12], [17], [20], [21] discuss fixed-time synchronization problem of neural networks. Fixed-time cluster synchronization problem of networks are dealt with in [22] and [23], respectively.…”

Section: Introductionmentioning

confidence: 99%

Fixed-Time Stabilization of Nonlinear System and its Application into General Neural Networks

Yan

2020

IEEE Access

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In this paper, fixed-time stability problem of nonlinear system is investigated. Firstly, a new fixed-time stability theorem is established, and a higher precise estimate of settling time is provided. Secondly, by theoretical deduction, the provided estimate is compared with the ones in existing fixedtime stabiity theorems. It is shown that our estimate is less conservative and more accurate than existing results. Moreover, as a practical application, fixed-time stabilization problem of general neural networks is studied. By developing a framework of protocol and applying the new fixed-time stability theorem, some new criteria are derived to solve fixed-time stabilization problem of general neural networks systems. Finally, two simulation examples are provided to verify the validity of our theoretical results. INDEX TERMS Fixed-time stability, Filippov solution, general neural networks, settling time.

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Section: Introductionmentioning

confidence: 99%

Fixed-Time Stabilization of Nonlinear System and its Application into General Neural Networks

Yan

2020

IEEE Access

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“…The simulation results show that the fixed-time synchronization of the drive-response FCNNs with time-varying delays and discontinuous activations can be achieved by using our method. In order to show that the estimation bound of the settling time is more effective and more accurate, we have performed a comparison with the results given in [22]. By computation, the maximum time point T # max = 0.11547s under the same set of parameters, and simulation results are presented in Figures 6-9, which illustrate that our estimations for the settling time function are less conservative and more accurate compared with the earlier work.…”

mentioning

confidence: 91%

“…Cao and Li [21] investigated the fixed-time synchronization problem for memoriter-based delayed CNNs. Yang et al [22] dealt with the fixed-time synchronizing control of the coupled complex neural networks based on memoriter where undirected spanning tree is contained in network topologies. Zheng et al [23] investigated the control problem of the fixed-time synchronization of delayed FCNNs based on memoriter.…”

Section: Introductionmentioning

confidence: 99%

Fixed-Time Synchronization of Fuzzy Cellular Neural Networks With Time-Varying Delays and Discontinuous Activations

Liu

Sun

2020

IEEE Access

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The present paper investigates the fixed-time synchronization for fuzzy cellular neural networks with time-varying delays and discontinuous activations. In the framework of differential inclusions, new and useful fixed-time synchronization criteria for the networks considered are established by utilizing the discontinuous state feedback control and constructing Lyapunov functionals, which significantly generalize and improve recent works in the literature. Numerical simulations are given to show the effectiveness of the fixed-time synchronization schemes. INDEX TERMS Delayed fuzzy cellular neural networks, discontinuous activations, state feedback control, fixed-time synchronization.

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“…Because of its application in secure communication, digital signal, neural network, and other fields, the synchronization of chaotic system is a fashionable subject in nonlinear science. Since the first scheme was carried out by Pecora and Carroll for the synchronization of two identical chaotic systems [27], a great diversity of methods have been proposed to synchronize chaotic systems, such as active control, adaptive control, impulsive control, sliding mode control, intermittent control, pinning control, and hybrid control [28][29][30][31]. Generally, a chaotic communication system can be constructed based on master-slave synchronization, where the message is modulated by the transmitting system and is then sent to the receiving system.…”

Section: Introductionmentioning

confidence: 99%

Dynamics and Synchronization of a Memristor‐Based Chaotic System with No Equilibrium

Yang

Zhou

et al. 2019

Complexity

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The topics of memristive system and synchronization are two hot fields of research in nonlinear dynamics. In this paper, we introduce a memristor-based chaotic system with no equilibrium. It is found that the memristor-based system under investigation exhibits fruitful dynamic behaviors such as coexisting bifurcation, multistability, transient chaos, and transient quasiperiod. Thus, it is difficult to reproduce the accurate dynamics of the system, which is highly advantageous in encryption and communication. Then, a simple intermittent control scheme with adaptive mechanism is developed to achieve complete synchronization for the introduced system. Because the output signal is transmitted intermittently to the receiver system, more channel capacity can be saved and the security performance can be improved naturally in practical communication.

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Fixed-time synchronization of coupled memristor-based neural networks with time-varying delays

Cited by 54 publications

References 36 publications

Fixed-Time Stabilization of Nonlinear System and its Application into General Neural Networks

Fixed-Time Stabilization of Nonlinear System and its Application into General Neural Networks

Fixed-Time Synchronization of Fuzzy Cellular Neural Networks With Time-Varying Delays and Discontinuous Activations

Dynamics and Synchronization of a Memristor‐Based Chaotic System with No Equilibrium

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