• DocumentCode
    303419
  • Title

    Hash-coding in CMAC neural networks

  • Author

    Wang, Zi-Qin ; Schiano, Jeffrey L. ; Ginsberg, Mark

  • Author_Institution
    Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA
  • Volume
    3
  • fYear
    1996
  • fDate
    3-6 Jun 1996
  • Firstpage
    1698
  • Abstract
    Hash-coding is used in CMAC neural networks to reduce the required memory, thereby making the CMAC practical to implement. In this paper the original motivation and rationale for using hash-coding in CMAC are questioned and it is shown that, contrary to the traditional believe, that hash-coding is unable to enhance CMAC´s approximation ability. A comparison between the CMAC performance obtained with two popular hash-coding methods is given
  • Keywords
    cerebellar model arithmetic computers; CMAC neural networks; hash-coding; memory requirement; performance evaluation; supervised learning; Hamming distance; Intelligent networks; Least squares approximation; Limit-cycles; Neural networks; Quantization; Supervised learning; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1996., IEEE International Conference on
  • Conference_Location
    Washington, DC
  • Print_ISBN
    0-7803-3210-5
  • Type

    conf

  • DOI
    10.1109/ICNN.1996.549156
  • Filename
    549156
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=303419