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A novel frequency-dependent hysteresis model based on improved neural Turing machine

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References

  1. Li Z W, Song Q K, Cheng L, et al. Snoring detection based on a stretchable strain sensor. Sci China Inf Sci, 2021, 64: 174201

    Article  Google Scholar 

  2. Wang J R, Zou Q Z. Rapid probe engagement and withdrawal with force minimization in atomic force microscopy: a learning-based online-searching approach. IEEE/ASME Trans Mechatron, 2020, 25: 581–593

    Article  Google Scholar 

  3. Li M, Xi N, Wang Y C, et al. Atomic force microscopy in probing tumor physics for nanomedicine. IEEE Trans Nanotechnol, 2019, 18: 83–113

    Article  ADS  Google Scholar 

  4. Tao Y D, Li H X, Zhu L M. Rate-dependent hysteresis modeling and compensation of piezoelectric actuators using Gaussian process. Sens Actuat A-Phys, 2019, 295: 357–365

    Article  CAS  Google Scholar 

  5. Ren Y, Chen M, Liu J Y. Bilateral coordinate boundary adaptive control for a helicopter lifting system with backlash-like hysteresis. Sci China Inf Sci, 2020, 63: 119203

    Article  MathSciNet  Google Scholar 

  6. Zhou Z P, Tan Y H, Liu X F. State estimation of dynamic systems with sandwich structure and hysteresis. Mech Syst Signal Process, 2019, 126: 82–97

    Article  ADS  Google Scholar 

  7. An Y, Meng H, Gao Y, et al. Application of machine learning method in optical molecular imaging: a review. Sci China Inf Sci, 2020, 63: 111101

    Article  Google Scholar 

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61633012, 62003172, 21933006).

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Authors and Affiliations

  1. Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, 300350, China

    Yinan Wu, Yongchun Fang, Zhi Fan & Cunhuan Liu

  2. Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, 300350, China

    Yinan Wu, Yongchun Fang, Zhi Fan & Cunhuan Liu

Authors
  1. Yinan Wu
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  2. Yongchun Fang
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  3. Zhi Fan
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  4. Cunhuan Liu
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Corresponding author

Correspondence to Yongchun Fang.

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Supporting information

Appendixes A–C. 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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Wu, Y., Fang, Y., Fan, Z. et al. A novel frequency-dependent hysteresis model based on improved neural Turing machine. Sci. China Inf. Sci. 66, 119203 (2023). https://doi.org/10.1007/s11432-020-3157-5

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  • DOI: https://doi.org/10.1007/s11432-020-3157-5

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