• DocumentCode
    315259
  • Title

    Improved sufficient convergence condition for the discrete-time cellular neural networks

  • Author

    Park, Sungjun ; Chae, Soo-Ik

  • Author_Institution
    Video Res. Center, Daewoo Electron. Co., Seoul, South Korea
  • Volume
    2
  • fYear
    1997
  • fDate
    9-12 Jun 1997
  • Firstpage
    1158
  • Abstract
    In this paper, we derive an improved sufficient convergence condition for discrete-time cellular neural networks (DTCNN) using the positive semidefinite (PSD) constraint and the boundary condition of DTCNN. The experimental results confirm that the derived condition offers a wider convergence range than the convergence condition of Fruehauf (1992). The new condition does not depend on the type of the nonlinear output function of the DTCNN
  • Keywords
    cellular neural nets; convergence; DTCNN; PSD constraint; convergence condition; discrete-time cellular neural networks; nonlinear output function; positive semidefinite constraint; Boundary conditions; Cellular neural networks; Convergence; Eigenvalues and eigenfunctions; Feedback; Hardware; Image processing; Piecewise linear techniques; Recurrent neural networks; Symmetric matrices;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks,1997., International Conference on
  • Conference_Location
    Houston, TX
  • Print_ISBN
    0-7803-4122-8
  • Type

    conf

  • DOI
    10.1109/ICNN.1997.616195
  • Filename
    616195
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=315259