• DocumentCode
    2356534
  • Title

    Progressive and constant-speed order filtering neural network

  • Author

    Chen, Chi-Ming ; Yang, Jar-Ferr

  • Author_Institution
    Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
  • fYear
    1994
  • fDate
    5-8 Dec 1994
  • Firstpage
    13
  • Lastpage
    17
  • Abstract
    In this paper, a new order filtering neural network, which can select a specific ordered value from all inputs, is developed and analyzed. The proposed neural net in two-layer structure iteratively converges to the solution with low and constant convergent speed, which is independent of the number of inputs. With progressive behavior, the proposed neural net obtains the more accurate result when the number of iterations increases if the derived convergent condition is satisfied. From the view points of convergence speed and hardware complexity, the proposed order filtering neural network is suitable for various applications
  • Keywords
    convergence of numerical methods; filtering theory; iterative methods; neural nets; convergence speed; hardware complexity; iteration; order filtering neural network; two-layer structure; Algorithm design and analysis; Circuits; Filtering algorithms; Image recognition; Neural network hardware; Neural networks; Nonlinear filters; Signal processing algorithms; Speech recognition; Switches;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 1994. APCCAS '94., 1994 IEEE Asia-Pacific Conference on
  • Conference_Location
    Taipei
  • Print_ISBN
    0-7803-2440-4
  • Type

    conf

  • DOI
    10.1109/APCCAS.1994.514516
  • Filename
    514516