Global exponential synchronization via nonlinear feedback control for delayed inertial memristor-based quaternion-valued neural networks with impulses

Lin, Dongyuan; Chen, Xiaofeng; Yu, Guoping; Li, Zhongshan; Xia, Yannan

doi:10.1016/j.amc.2021.126093

Cited by 36 publications

(24 citation statements)

References 38 publications

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“…When the damping value exceeds the critical state, the dynamic properties of each neuron in the network model will fundamentally change, which provides us with a powerful tool to generate complex dynamic behaviors such as chaos and bifurcation. At present, many research results have been achieved on the neural network with inertial term, mainly including the following: exponential stability of inertial neural network [4], anti-periodic solution [5], global stability [6], global convergence analysis [7], exponential dissipativeness [8], stability of Cohen-Grossberg Neural Networks [9], global asymptotic stability and robust stability [10], finite time synchronization [11], fixed time synchronization [12], and exponential synchronization [13].…”

Section: Introductionmentioning

confidence: 99%

Asymptotic and Finite-Time Synchronization of Fractional-Order Memristor-Based Inertial Neural Networks with Time-Varying Delay

Sun

Liu

2022

Fractal Fract

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This paper emphasized on studying the asymptotic synchronization and finite synchronization of fractional-order memristor-based inertial neural networks with time-varying latency. The fractional-order memristor-based inertial neural network model is offered as a more general and flexible alternative to the integer-order inertial neural network. By utilizing the properties of fractional calculus, two lemmas on asymptotic stability and finite-time stability are provided. Based on the two lemmas and the constructed Lyapunov functionals, some updated and valid criteria have been developed to achieve asymptotic and finite-time synchronization of the addressed systems. Finally, the effectiveness of the proposed method is demonstrated by a number of examples and simulations.

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Section: Introductionmentioning

confidence: 99%

Asymptotic and Finite-Time Synchronization of Fractional-Order Memristor-Based Inertial Neural Networks with Time-Varying Delay

Sun

Liu

2022

Fractal Fract

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“…[14][15][16] Dongyuan Lin and Jinde Cao studied quaternion-valued inertial neural network in 2019 and 2021. [17][18] Adel M.Alimi, Xinsong Yang and Yongqing Yang studied inertial neural network with proportional delay in 2018 and 2020. [19][20][21] Cheng Hu studied Cohen-Grossberg neural network with proportional delay in 2017.…”

Section: Introductionmentioning

confidence: 99%

Exponential and finite/fixed-time synchronization of quaternion-valued Cohen-Grossberg inertial neural networks with proportional-delayed by non-separated method with imperfect order theory

Xiong

et al. 2022

Preprint

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In this paper, exponential and finite/fixed-time synchronization of quaternion-valued Cohen-Grossberg inertial neural network with proportional-delayed is investigated. In order to study the convergence of quaternion-value system, we proposed the imperfect order theory. Accordingly quaternion-value Lyapunov function are proposed and utilized to study the synchronization of the neural network. Several lemmas are introduced and proved for the later synchronization study. Unlike the most commonly used separated method for quaternion-value network, with the help of our imperfect order theory, we adopt a non-separated method to construct the controller and analyze the quaternion value Lyapunov function directly. Numerical simulations are presented to indicate the effectiveness of the proposed method.

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“…In consequence, it is essential to make use of some control strategies to make MNNs passive [24,25]. To ensure exponential synchronization for MNNs, Lin et al [24] developed a nonlinear feedback controller. Zhang et al [25] derived some sufficient conditions for achieving finite time synchronization based on the feedback control.…”

Section: Introductionmentioning

confidence: 99%

“…It is worth noticing that the passivity of MNNs usually cannot be achieved on their own [23]. In consequence, it is essential to make use of some control strategies to make MNNs passive [24,25]. To ensure exponential synchronization for MNNs, Lin et al [24] developed a nonlinear feedback controller.…”

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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Global exponential synchronization via nonlinear feedback control for delayed inertial memristor-based quaternion-valued neural networks with impulses

Cited by 36 publications

References 38 publications

Asymptotic and Finite-Time Synchronization of Fractional-Order Memristor-Based Inertial Neural Networks with Time-Varying Delay

Asymptotic and Finite-Time Synchronization of Fractional-Order Memristor-Based Inertial Neural Networks with Time-Varying Delay

Exponential and finite/fixed-time synchronization of quaternion-valued Cohen-Grossberg inertial neural networks with proportional-delayed by non-separated method with imperfect order theory

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

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