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Robust sampled-data synchronization of chaotic fractional variable order neural networks with time delays

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Abstract

This paper investigates the synchronization problem of master and slave fractional variable-order delayed neural networks (FVODNNs) under the sampled-data control (SDC) scheme. In contrast to existing fractional-order neural networks (NNs), fractional variable-order (FVO)-based inequality is derived in this work to find the derivative of the proposed Lyapunov Krasovskii functional (LKF). In addition, time-varying delays and parametric uncertainties are taken into account. To achieve synchronization, an input delay-based SDC scheme is implemented in the control input of the slave FVODNNs. Based on the FVO derivative, a new class of LKF is constructed that includes information about time-varying delays and sampling instants. In order to guarantee the asymptotic stability of the error states of FVODNNs, the corresponding new synchronization conditions are derived in the form of a linear matrix inequality. Finally, a numerical example and their simulation results are given to show the synchronization between the master and slave FVODNNs under the SDC control scheme and their superiority.

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Authors and Affiliations

  1. Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu, 600127, India

    R. Kiruthika & A. Manivannan

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Kiruthika, R., Manivannan, A. Robust sampled-data synchronization of chaotic fractional variable order neural networks with time delays. Eur. Phys. J. Spec. Top. (2024). https://doi.org/10.1140/epjs/s11734-024-01242-y

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