A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 10 Issue 1
Jan.  2023

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2023  >  10(1): 298-300
C. Z. Jiang and X. C. Xiao, “Norm-based adaptive coefficient ZNN for solving the time-dependent algebraic Riccati equation,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 298–300, Jan. 2023. doi: 10.1109/JAS.2023.123057
Citation: C. Z. Jiang and X. C. Xiao, “Norm-based adaptive coefficient ZNN for solving the time-dependent algebraic Riccati equation,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 298–300, Jan. 2023. doi: 10.1109/JAS.2023.123057
shu

Norm-Based Adaptive Coefficient ZNN for Solving the Time-Dependent Algebraic Riccati Equation

doi: 10.1109/JAS.2023.123057

  • School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China

More Information
    Abstract
    • FullText(HTML)
  • loading
    • References(16)
  • [1]
    A. R. R. Narváez and E. F. Costa, “Control of continuous-time linear systems with Markov jump parameters in reverse time,” IEEE Trans. Autom. Control., vol. 65, no. 5, pp. 2265–2271, May 2020. doi: 10.1109/TAC.2019.2944919
    [2]
    L. Lin and M. Xin, “Computational enhancement of the SDRE scheme: General theory and robotic control system,” IEEE Trans. Robot., vol. 36, no. 3, pp. 875–893, Jun. 2020. doi: 10.1109/TRO.2020.2976330
    [3]
    T. Nguyen and Z. Gajic, “Solving the matrix differential Riccati equation: A Lyapunov equation approach,” IEEE Trans. Autom. Control., vol. 55, no. 1, pp. 191–194, Jan. 2010. doi: 10.1109/TAC.2009.2033841
    [4]
    F. J. Vargas and R. A. González, “On the existence of a stabilizing solution of modified algebraic Riccati equations in terms of standard algebraic Riccati equations and linear matrix inequalities,” IEEE Contr. Syst. Lett., vol. 4, no. 1, pp. 91–96, Jan. 2020. doi: 10.1109/LCSYS.2019.2921998
    [5]
    Y. Zhang, S. Satapathy, D. Wu, D. Guttery, J. Gorriz, and S. Wang, “Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling,” Complex Intel Syst., vol. 7, no. 3, pp. 1295–1310, Nov. 2020.
    [6]
    L. Xiao and Y. He, “A noise-suppression ZNN model with new variable parameter for dynamic Sylvester equation,” IEEE Trans. Ind. Informat., vol. 17, no. 11, pp. 7513–7522, Nov. 2021. doi: 10.1109/TII.2021.3058343
    [7]
    S. Wang, Z. Zhu, and Y. Zhang, “PSCNN: PatchShuffle convolutional neural network for COVID-19 explainable diagnosis,” Front. Public Health., vol. 9, p. 768278, Oct. 2021.
    [8]
    H. Liu, T. Wang, and D. Guo, “Design and validation of zeroing neural network to solve time-varying algebraic Riccati equation,” IEEE Access., vol. 8, pp. 211315–211323, Nov. 2020. doi: 10.1109/ACCESS.2020.3039253
    [9]
    S. Wang, S. Satapathy, D. Anderson, S. Chen, and Y. Zhang, “Deep fractional max pooling neural network for COVID-19 recognition,” Front. Public Health., vol. 9, p. 726144, Aug. 2021.
    [10]
    S. Wang, M. Khan, Y. Zhang, “VISPNN: VGG-inspired stochastic pooling neural network,” Comput. Mater. Contin., vol. 70, no. 2, pp. 3081–3097, Sep. 2022.
    [11]
    L. Jin, L. Wei, and S. Li, “Gradient-based differential neural-solution to time-dependent nonlinear optimization,” IEEE Trans. Autom. Control., DOI: 10.1109/TAC.2022.3144135.
    [12]
    W. Li, X. Ma, J. Luo, and L. Jin, “A strictly predefined-time convergent neural solution to equality- and inequality-constrained time-variant quadratic programming,” IEEE Trans. Syst.,Man,Cybern. Syst., vol. 51, no. 7, pp. 4028–4039, Jul. 2021. doi: 10.1109/TSMC.2019.2930763
    [13]
    Y. Zhang, Y. Ling, M. Yang, S. Yang, and Z. Zhang, “Inverse-free discrete ZNN models solving for future matrix pseudoinverse via combination of extrapolation and ZeaD formulas,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 6, pp. 2663–2675, Jun. 2021. doi: 10.1109/TNNLS.2020.3007509
    [14]
    L. Jin, X. Zheng, and X. Luo, “Neural dynamics for distributed collaborative control of manipulators with time delays,” IEEE/CAA J. Autom. Sinica., vol. 9, no. 5, pp. 854–863, May 2022. doi: 10.1109/JAS.2022.105446
    [15]
    Y. Liufu, L. Jin, J. Xu, X. Xiao, and D. Fu, “Reformative noise-immune neural network for equality-constrained optimization applied to image target detection,” IEEE Trans. Emerg. Top. Comput., DOI: 10.1109/TETC.2021.3057395.
    [16]
    C. Jiang, X. Xiao, D. Liu, H. Huang, H. Xiao, and H. Lu, “Nonconvex and bound constraint zeroing neural network for solving time-varying complex-valued quadratic programming problem,” IEEE Trans. Ind. Informat., vol. 17, no. 10, pp. 6864–6874, Oct. 2021. doi: 10.1109/TII.2020.3047959
    • Related Articles
    • Supplements(0)

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(3)  / Tables(1)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (326) PDF downloads(37) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return