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Preassigned-Time Synchronization of Delayed Fuzzy Cellular Neural Networks with Discontinuous Activations

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Abstract

In this paper, the problem of the preassigned-time synchronization is studied for a class of delayed fuzzy cellular neural networks with discontinuous activations via preassigned-time control. Above all, with the help of the existing classical fixed-time stability theory and via variable substitutions, a new fixed-time stability result is established, and the derived estimation of settling times are tighter than the existing ones. Subsequently, as applications of the new fixed-time stability results, a novel preassigned-time stability theorem is derived, and some novel and useful sufficient criteria are obtained to guarantee that the addressed neural networks achieve preassigned-time synchronization. The preassigned synchronization time is not only independent of the initial value, system and controller parameters, but also can be preassigned in advance based on the actual needs. Additionally, to suppress or eliminate the chattering phenomenon, a saturation function is designed to replace the sign function in the controller, and its effectiveness has been verified by numerical simulation. Especially, one of the power exponents of the two differently designed preassigned-time controllers can change smartly instead of being a fixed constant. Lastly, two numerical examples with simulations are given to present the effectiveness of the obtained results.

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Data Availibility

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the National Natural Science Foundation of People’s Republic of China (Grant No. 12061055, Grant No. 12101454), Ningxia Natural Science Foundation (Grant No. 2020AAC03131), and Zunyi Normal college 2017 academic new talent cultivation and innovation exploration project(Qiankehe platform talent [2017] Grant No. 5727-24).

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  1. School of Mathematics and Statistics, Ningxia University, Yinchuan, 750021, Ningxia, People’s Republic of China

    Hao Pu & Fengjun Li

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  1. Hao Pu
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  2. Fengjun Li
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Correspondence to Fengjun Li.

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Pu, H., Li, F. Preassigned-Time Synchronization of Delayed Fuzzy Cellular Neural Networks with Discontinuous Activations. Neural Process Lett 54, 4265–4296 (2022). https://doi.org/10.1007/s11063-022-10808-7

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