• DocumentCode
    1749242
  • Title

    Stability analysis of nonlinear systems using high order derivatives of universal learning networks

  • Author

    Hirasawa, Kotaro ; Yu, Yunqing ; Hu, Jinglu ; Murata, Junichi

  • Author_Institution
    Dept. of Electr. & Electron. Syst. Eng., Kyushu Univ., Fukuoka, Japan
  • Volume
    2
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    1273
  • Abstract
    In this paper, a stability analysis method based on the higher order derivatives of universal learning networks is proposed. In the proposed method, the following are proposed. First, if the absolute values of the first order derivatives of any nodes with respect to any initial disturbances approach zero at time infinity, then the trajectory is locally asymptotically stable. Next, the locally asymptotically stable region, where asymptotic stability is secured approximately, is obtained by comparing the first order derivatives and higher order derivatives
  • Keywords
    asymptotic stability; control system analysis; learning systems; neural nets; nonlinear systems; asymptotic stability; high order derivatives; nonlinear systems; universal learning networks; Asymptotic stability; Delay effects; Differential equations; H infinity control; Input variables; Lyapunov method; Nonlinear dynamical systems; Nonlinear systems; Stability analysis; Systems engineering and theory;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7044-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2001.939544
  • Filename
    939544