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Adaptive event-trigger-based sampled-data stabilization of complex-valued neural networks: a real and complex LMI approach

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61573008, 61973199, 61973200), Taishan Scholar Project of Shandong Province of China, and SDUST Research Fund (Grant No. 2018 TDJH101).

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Authors and Affiliations

  1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

    Xiaohong Wang & Yuxia Li

  2. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, 266590, China

    Zhen Wang

  3. School of Mathematical Science, Liaocheng University, Liaocheng, 252059, China

    Jianwei Xia

  4. School of Electrical Engineering and Information, Anhui University of Technology, Ma’anshan, 243002, China

    Hao Shen

Authors
  1. Xiaohong Wang
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  2. Zhen Wang
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  3. Jianwei Xia
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  4. Hao Shen
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  5. Yuxia Li
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Corresponding authors

Correspondence to Zhen Wang or Hao Shen.

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Appendixes A–D. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Adaptive event-trigger-based sampled-data stabilization of complex-valued neural networks: a real and complex LMI approach

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Wang, X., Wang, Z., Xia, J. et al. Adaptive event-trigger-based sampled-data stabilization of complex-valued neural networks: a real and complex LMI approach. Sci. China Inf. Sci. 66, 149203 (2023). https://doi.org/10.1007/s11432-020-3237-x

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  • DOI: https://doi.org/10.1007/s11432-020-3237-x

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