The synchronization of fractional chaotic systems with WRBF neural network

Wang, Shaofu

doi:10.1140/epjp/s13360-022-03183-8

Eur. Phys. J. Plus

2022

DOI: 10.1140/epjp/s13360-022-03183-8

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The synchronization of fractional chaotic systems with WRBF neural network

Shaofu Wang

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2024

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Cited by 2 publications

References 21 publications

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Adaptive control for memristive system via compensatory controller and Chebyshev neural network

Wang

2024

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In this paper, based on linear matrix inequality technique, a simple controller and a compensatory controller are designed. It can track arbitrary fixed points and any periodic orbits. In addition, a synchronization control method via Chebyshev neural network with external disturbances is proposed. An adaptive controller is given. The Chebyshev neural network is used to approximate the uncertain nonlinear function and the adaptive law is used to adjust the corresponding parameters in the system. Taking a 4D memristive chaotic system as examples, the results are in consistent with the simulations. From a framework and control theoretical point of view, the proposed synchronization approach via compensation controller and Chebyshev neural network is firstly presented. From an application point of view, the proposed scheme can simplify the complexity of controller design. It is promising in many applications for mem-systems as secure communications and neural networks.

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Adaptive control for memristive system via compensatory controller and Chebyshev neural network

Wang

2024

Sci Rep

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Cluster Dependency and Synchronization of Multilayer Directed Networks From a Quotient Graph Perspective

XIE,

ZHAI,

LIU

et al. 2024

Advs. Complex Syst.

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This paper studies cluster dependence and synchronization in multilayer directed networks. Based on network quotient graphs, we provide a more efficient way to identify dependencies between clusters. By considering the relationships and interactions between clusters at a higher level of abstraction, this analysis can reduce the computational effort without requiring extensive computation on the original network. Furthermore, this paper investigates the impact of structural perturbations on network synchronization capabilities within a multilayer framework. Specifically, for the case where perturbations are applied to the dominant group, we study how changes in the network structure affect its cluster synchronization (CS) behavior. Finally, we employe a multilayer neural network model to validate the findings presented in this paper.

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The synchronization of fractional chaotic systems with WRBF neural network

Cited by 2 publications

References 21 publications

Adaptive control for memristive system via compensatory controller and Chebyshev neural network

Adaptive control for memristive system via compensatory controller and Chebyshev neural network

Cluster Dependency and Synchronization of Multilayer Directed Networks From a Quotient Graph Perspective

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