Finite-time Synchronization of Fractional-Order Neural Networks With Time-Varying Delays

Li, Yunliu; Xu, Minglin; Sun, Junwei; Liu, Na; Yu, Peizhao; Liu, Peng

doi:10.1109/icist52614.2021.9440646

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…In recent years, many classical results focusing on their dynamical behaviors, such as stability, synchronization, and passivity, have been obtained. For example, in [7], the global stability of complex valued FOCNNs with nodes in unequal dimensions and time delays was analyzed using comparison theory; in [8], the stability analysis of FOCNNs with time delays was investigated; in addition, the stability of two three-dimensional FOCNNs with different ring structures and time delays were analyzed; in [9], the authors studied the quantized output feedback synchronization of FOCNNs with output coupling; in [10], the authors considered the finite-time synchronization of FOCNNs with time-varying delays; the results were proved to be applicable to the FOCNNs without time delays and integer-order neural networks; in [11], the authors innovatively introduced the concept of finite-time passivity for FOCNNs with multiple state coupling or multiple derivative coupling; in [12], based on the existing passivity definition, the authors proposed the concepts of finite-time input strict passivity, finite-time output strict passivity, and finitetime strict passivity for FOCNNs; in addition, novel delay-dependent and order-dependent sufficient conditions ensuring the passivity performances were obtained for FOCNNs. More interesting results can be found in [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Positivity and Stability of Fractional-Order Coupled Neural Network with Time-Varying Delays

Gong,

Qiu,

Shen

2023

Electronics

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This brief paper analyzes the positivity and asymptotic stability of incommensurate fractional-order coupled neural networks (FOCNNs) with time-varying delays. Under a reasonable assumption about the activation functions of neurons, a sufficient and necessary condition is proposed to guarantee that FOCNNs are positive systems. Furthermore, the sufficient and necessary condition ensuring the asymptotic stability of FOCNNs is also given via introducing a linear auxiliary system. Finally, a simulation experiment was carried out to justify the effectiveness of the derived results.

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Section: Introductionmentioning

confidence: 99%

Positivity and Stability of Fractional-Order Coupled Neural Network with Time-Varying Delays

Gong,

Qiu,

Shen

2023

Electronics

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Multi-State Synchronization of Chaotic Systems with Distributed Fractional Order Derivatives and Its Application in Secure Communications

Javan

Zare

Alizadehsani

2022

BDCC

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This study investigates multiple synchronizations of distributed fractional-order chaotic systems. These systems consider unknown parameters, disturbance, and time delays. A robust adaptive control method is designed for multistage distributed fractional-order chaotic systems. In this paper, system parameters are changed step by step. Using Lyapunov’s function, while the synchronization error convergence to zero is guaranteed, adaptive rules are designed to estimate the parameters. Then, a secure communication scheme is proposed using the new chaotic masking method. Finally, the simulations are performed on a chaotic system of distributed Duffing fractional order. The results show the high efficiency of the proposed synchronization scheme using robust adaptive control, despite the parametric uncertainties, external disturbance, and variable and unknown time delays. Then, the simulations were performed on the sinusoidal signals of the message in the application of secure communications. The results showed the success of the proposed masking scheme with synchronization in coding and decoding information.

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Adaptive Quantized Synchronization of Fractional-Order Output-Coupling Multiplex Networks

Bai

2022

Fractal Fract

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This paper is devoted to investigating the synchronization of fractional-order output-coupling multiplex networks (FOOCMNs). Firstly, a type of fractional-order multiplex network is introduced, where the intra-layer coupling and the inter-layer coupling are described separately, and nodes communicate with each other by their outputs, which is more realistic when the node states are unmeasured. By using the Lyapunov method and the fractional differential inequality, sufficient conditions are provided for achieving asymptotic synchronization based on the designed adaptive control, where the synchronized state of each layer is different. Furthermore, a quantized adaptive controller is developed to realize the synchronization of FOOCMNs, which effectively reduces signal transmission frequency and improves the effective utilization rate of network resources. Two numerical examples are given at last to support the theoretical analysis.

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Finite-time Synchronization of Fractional-Order Neural Networks With Time-Varying Delays

Cited by 3 publications

References 24 publications

Positivity and Stability of Fractional-Order Coupled Neural Network with Time-Varying Delays

Positivity and Stability of Fractional-Order Coupled Neural Network with Time-Varying Delays

Multi-State Synchronization of Chaotic Systems with Distributed Fractional Order Derivatives and Its Application in Secure Communications

Adaptive Quantized Synchronization of Fractional-Order Output-Coupling Multiplex Networks

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