• DocumentCode
    3266828
  • Title

    Rough-winner-take-all self-organizing neural network for hardware oriented vector quantization algorithm

  • Author

    Tamukoh, Hakaru ; Koga, Takanori ; Horio, Keiichi ; Yamakawa, Takeshi

  • Author_Institution
    Kyushu Inst. of Technol., Kitakyushu
  • fYear
    2007
  • fDate
    5-8 Aug. 2007
  • Firstpage
    349
  • Lastpage
    352
  • Abstract
    In this paper, we propose a new vector quantization method for an efficient digital hardware implementation. The basic algorithm of the proposed method is similar to K-means clustering which is the simplest vector quantization. The only different point is that the proposed method employs a rough-winner-take-all as the substitute of ordinary winner-take-all. The simulation results show that quantization performance of the proposed method is nearly equal to neural gas which is an excellent vector quantization. Besides, the proposed method features low hardware complexity as compared to neural gas.
  • Keywords
    computational complexity; self-organising feature maps; vector quantisation; K-means clustering; digital hardware implementation; hardware complexity; hardware oriented vector quantization algorithm; rough-winner-take-all self-organizing neural network; Neural network hardware; Neural networks; Vector quantization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 2007. MWSCAS 2007. 50th Midwest Symposium on
  • Conference_Location
    Montreal, Que.
  • ISSN
    1548-3746
  • Print_ISBN
    978-1-4244-1175-7
  • Electronic_ISBN
    1548-3746
  • Type

    conf

  • DOI
    10.1109/MWSCAS.2007.4488604
  • Filename
    4488604