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Fixed/predefined-time synchronization of fuzzy neural networks with stochastic perturbations

Author

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  • Abudusaimaiti, Mairemunisa
  • Abdurahman, Abdujelil
  • Jiang, Haijun
  • Hu, Cheng

Abstract

In this work, fixed-time (FXT) and predefined-time (PDT) synchronization issue of a type of fuzzy cellular neural networks (FCNNs) with stochastic perturbations is concerned via using some improved FXT stability results. First, we introduced some generalized FXT and PTD stability lemmas for a class of nonlinear stochastic differential equation. Then, based on these novel FXT stability results, we investigated the FXT synchronization of a type of FCNNs with stochastic fluctuations by designing two kinds of simple controllers, which not use a linear term −kiei(t) and provide more accurate settling time (ST) estimations compared to early published literatures. Furthermore, we have also analyzed the PDT synchronization of considered stochastic fuzzy networks via developing several nontrivial PDT control protocols and applying some inequality methods. Finally, we provided one numerical example with its Matlab simulations to show the feasibility of our developed results. We believe that methodology used in this paper can provide some new guidance to FXT and PDT synchronization studies of other nonlinear stochastic systems with or without fuzzy templates.

Suggested Citation

  • Abudusaimaiti, Mairemunisa & Abdurahman, Abdujelil & Jiang, Haijun & Hu, Cheng, 2022. "Fixed/predefined-time synchronization of fuzzy neural networks with stochastic perturbations," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    • Handle: RePEc:eee:chsofr:v:154:y:2022:i:c:s0960077921009504
    DOI: 10.1016/j.chaos.2021.111596
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    References listed on IDEAS

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    1. Zhao, Yong & Ren, Shanshan & Kurths, Jürgen, 2021. "Finite-time and fixed-time synchronization for a class of memristor-based competitive neural networks with different time scales," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    2. Feng, Liang & Hu, Cheng & Yu, Juan & Jiang, Haijun & Wen, Shiping, 2021. "Fixed-time Synchronization of Coupled Memristive Complex-valued Neural Networks," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    3. Sweetha, S. & Sakthivel, R. & Harshavarthini, S., 2021. "Finite-time synchronization of nonlinear fractional chaotic systems with stochastic actuator faults," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    4. Song, Yi & Xu, Wei, 2021. "Asymmetric Lévy noise changed stability in a gene transcriptional regulatory system," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    5. Assali, El Abed, 2021. "Predefined-time synchronization of chaotic systems with different dimensions and applications," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
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    Cited by:

    1. Abdurahman, Abdujelil & Abudusaimaiti, Mairemunisa & Jiang, Haijun, 2023. "Fixed/predefined-time lag synchronization of complex-valued BAM neural networks with stochastic perturbations," Applied Mathematics and Computation, Elsevier, vol. 444(C).
    2. Hayrengul Sadik & Abdujelil Abdurahman & Rukeya Tohti, 2023. "Fixed-Time Synchronization of Reaction-Diffusion Fuzzy Neural Networks with Stochastic Perturbations," Mathematics, MDPI, vol. 11(6), pages 1-15, March.
    3. Xue, Haibo & Liu, Xinghua, 2023. "A novel fast terminal sliding mode with predefined-time synchronization," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
    4. Mairemunisa Abudusaimaiti & Abuduwali Abudukeremu & Amina Sabir, 2023. "Fixed/Preassigned-Time Stochastic Synchronization of Complex-Valued Fuzzy Neural Networks with Time Delay," Mathematics, MDPI, vol. 11(17), pages 1-18, September.
    5. Zhang, Lingzhong & Zhong, Jie & Lou, Jungang & Liu, Yang & Lu, Jianquan, 2023. "Bipartite secure synchronization for dynamic networks under deception attacks via delay-dependent impulsive control," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).

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