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LMI Conditions for Fractional Exponential Stability and Passivity Analysis of Uncertain Hopfield Conformable Fractional-Order Neural Networks

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Abstract

This article studies the problem of fractional exponential stability and passivity analysis for Hopfield conformable fractional-order neural networks (CFONNs) subject to uncertainties. First, we derive some less conservative conditions to ensure the fractional exponential stability of Hopfield CFONNs by using the Lyapunov functional method combined with the linear matrix inequality (LMI) approach. Then, by introducing a new definition of passivity analysis for Hopfield CFONNs, an LMI condition is proposed to ensure the passivity analysis of the considered system. Numerical examples are carried out to verify the correctness of the obtained results.

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Data Availability Statement

All data generated or analyzed during this study are included in this article.

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Acknowledgements

The authors sincerely thank the Associate Editor and anonymous reviewers for their constructive comments that helped improve the quality and presentation of this paper. The authors also thank Dr. N.T. Thanh for helpful comments and discussion in improving Sect.4. The research of Mai Viet Thuan is funded by the Ministry of Education and Training of Vietnam under grant number B2022-MDA-02

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Authors and Affiliations

  1. Department of Mathematics and Informatics, TNUS–University of Sciences, Thainguyen, Vietnam

    Nguyen Thi Thanh Huyen & Mai Viet Thuan

  2. Faculty of Fundamental Science, Hanoi University of Industry, 298 Cau Dien street, Bac Tu Liem District, Hanoi, Vietnam

    Nguyen Huu Sau

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  1. Nguyen Thi Thanh Huyen
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  2. Nguyen Huu Sau
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  3. Mai Viet Thuan
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Correspondence to Mai Viet Thuan.

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Huyen, N.T.T., Sau, N.H. & Thuan, M.V. LMI Conditions for Fractional Exponential Stability and Passivity Analysis of Uncertain Hopfield Conformable Fractional-Order Neural Networks. Neural Process Lett 54, 1333–1350 (2022). https://doi.org/10.1007/s11063-021-10683-8

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  • DOI: https://doi.org/10.1007/s11063-021-10683-8

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