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Quasi-Projective and Mittag-Leffler Synchronization of Discrete-Time Fractional-Order Complex-Valued Fuzzy Neural Networks | Neural Processing Letters Skip to main content

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Quasi-Projective and Mittag-Leffler Synchronization of Discrete-Time Fractional-Order Complex-Valued Fuzzy Neural Networks

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Abstract

In this paper, we consider a type of discrete-time fractional-order complex-valued fuzzy neural networks. First, based on symbolic functions and complex-valued theory, two new inequalities are established to study synchronization problems of networks. Secondly, two simple and original control strategies including linear feedback controller and adaptive controller which can better reduce the control cost, are designed to guarantee quasi-projective synchronization and Mittag-Leffler synchronization of the considered networks, then sufficient synchronization criteria with simplified algebraic conditions are established on the basis of our established lemmas and some inequality techniques. Finally, numerical simulations are given to check effectiveness of the proposed results.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 12262035, 12261087), Tianshan Youth Program-Training Program for Excellent Young Scientific and Technological Talents (Grant No. 2019Q017).

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Authors and Affiliations

  1. College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046, China

    Yingying Xu, Hong-Li Li, Long Zhang, Cheng Hu & Haijun Jiang

Authors
  1. Yingying Xu
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  2. Hong-Li Li
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  3. Long Zhang
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  4. Cheng Hu
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  5. Haijun Jiang
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Contributions

Yingying Xu: Methodology, Writing-original draft. Hong-Li Li: Supervision, Conceptualization, Methodology, Writing-review & editing, Funding acquisition. Long Zhang: Formal analysis, Methodology. Cheng Hu: Software. Haijun Jiang: Formal analysis.

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Correspondence to Hong-Li Li.

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Xu, Y., Li, HL., Zhang, L. et al. Quasi-Projective and Mittag-Leffler Synchronization of Discrete-Time Fractional-Order Complex-Valued Fuzzy Neural Networks. Neural Process Lett 55, 6657–6677 (2023). https://doi.org/10.1007/s11063-023-11153-z

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