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Fixed-time synchronization of complex-valued memristive competitive neural networks based on two novel fixed-time stability theorems

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Abstract

The fixed-time synchronization (FTS) of complex-valued memristive competitive neural networks (CVMCNNs) with mixed delays is the main topic of this paper. Firstly, two new fixed-time (FT) stability criteria are obtained. In the FT stability criterion, the exponent is usually positive, but the exponent can be less than zero and varies with the error state in this paper. Then, based on two new stability theorems, the FTS of CVMCNNs is studied in the sense of 1-norm and 2-norm, respectively, without dividing the complex-valued state variables into the real part (RP) and the imaginary part (IP). Finally, two numerical simulation examples are given to further demonstrate the validity and superiority of our results.

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

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Acknowledgements

This research was supported in part by the National Natural Science Foundation of China (61876192), the Fundamental Research Funds for the Central Universities (CZT20020) and Academic Team in Universities (KTZ20051) for their financial support.

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Authors and Affiliations

  1. Three Gorges Mathematical Research Center, China Three Gorges University, Yichang, 443002, Hubei, China

    Chenguang Xu

  2. Institute of Nonlinear Complex Systems, China Three Gorges University, Yichang, 443002, Hubei, China

    Minghui Jiang

  3. College of Mathematics and Statistics, South Central University for Nationalities, Wuhan, 430074, Hubei, China

    Junhao Hu

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  1. Chenguang Xu
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  2. Minghui Jiang
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  3. Junhao Hu
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Correspondence to Minghui Jiang.

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Xu, C., Jiang, M. & Hu, J. Fixed-time synchronization of complex-valued memristive competitive neural networks based on two novel fixed-time stability theorems. Neural Comput & Applic 35, 22605–22620 (2023). https://doi.org/10.1007/s00521-023-08874-6

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