A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 11 Issue 6
Jun.  2024

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 4% (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
J. Liu, S. Li, and R. Liu, “Recurrent neural network inspired finite-time control design,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1527–1529, Jun. 2024. doi: 10.1109/JAS.2023.123297
Citation: J. Liu, S. Li, and R. Liu, “Recurrent neural network inspired finite-time control design,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1527–1529, Jun. 2024. doi: 10.1109/JAS.2023.123297

Recurrent Neural Network Inspired Finite-Time Control Design

doi: 10.1109/JAS.2023.123297
More Information
  • loading
  • [1]
    W. Yu, “Nonlinear system identification using discrete-time recurrent neural networks with stable learning algorithms,” Inf. Sci., vol. 158, pp. 131–147, 2004. doi: 10.1016/j.ins.2003.08.002
    [2]
    W. Yu and X. Li, “Some new results on system identification with dynamic neural networks,” IEEE Trans. Neural Netw., vol. 12, no. 2, pp. 412–417, 2001. doi: 10.1109/72.914535
    [3]
    T. Mikolov, A. Joulin, S. Chopra, M. Mathieu, and M. Ranzato, “Learning longer memory in recurrent neural networks,” arXiv preprint arXiv: 1412.7753, 2014.
    [4]
    Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradient descent is difficult,” IEEE Trans. Neural Netw., vol. 5, no. 2, pp. 157–166, 1994. doi: 10.1109/72.279181
    [5]
    H. Salehinejad, S. Sankar, J. Barfett, E. Colak, and S. Valaee, “Recent advances in recurrent neural networks,” arXiv preprint arXiv: 1801.01078, 2017.
    [6]
    J. Wang, L. Zhang, Q. Guo, and Z. Yi, “Recurrent neural networks with auxiliary memory units,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 5, pp. 1652–1661, 2018. doi: 10.1109/TNNLS.2017.2677968
    [7]
    J. Turek, S. Jain, V. Vo, M. Capotă, A. Huth, and T. Willke, “Approximating stacked and bidirectional recurrent architectures with the delayed recurrent neural network,” in Proc. Int. Conf. Mach. Learn., PMLR, 2020, pp. 9648−9658.
    [8]
    X. Liu, J. Zhou, and H. Qian, “Short-term wind power forecasting by stacked recurrent neural networks with parametric sine activation function,” Electr. Power Syst. Res., vol. 192, p. 107011, 2021. doi: 10.1016/j.jpgr.2020.107011
    [9]
    Y. Liu, H. Li, Z. Zuo, X. Li, and R. Lu, “An overview of finite/fixed-time control and its application in engineering systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2106–2120, Dec. 2022. doi: 10.1109/JAS.2022.105413
    [10]
    S. P. Bhat and D. S. Bernstein, “Finite-time stability of continuous autonomous systems,” SIAM J. Control Optim., vol. 38, pp. 751–766, 2000. doi: 10.1137/S0363012997321358
    [11]
    A. Perrusquía and W. Yu, “Identification and optimal control of nonlinear systems using recurrent neuralnetworks and reinforcement learning: An overview,” Neurocomput., vol. 438, pp. 145–154, 2021. doi: 10.1016/j.neucom.2021.01.096
    [12]
    R. Liu, S. Li, and S. Ding, “Nested saturation control for overhead crane systems,” Trans. Inst. Meas. Control, vol. 34, no. 7, pp. 862–875, 2012. doi: 10.1177/0142331211423285

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)

    Article Metrics

    Article views (214) PDF downloads(53) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return