• DocumentCode
    3327100
  • Title

    Multilayer perceptron and vector quantization

  • Author

    Xinwen, Wang ; Lihe, Zou ; Zhenya, He

  • Author_Institution
    DSP Lab., Southeast Univ., Jiangsu, China
  • fYear
    1991
  • fDate
    28 Oct-1 Nov 1991
  • Firstpage
    1361
  • Abstract
    The exponential encoding complexity has been the bottleneck drawback of vector quantization (VQ) in its applications. A kind of neural network multilayer perceptron (MLP) is introduced to attack the bottleneck. Based on the analysis of the VQ structure and the function of the MLP, an important conclusion on the relationship between the task and the scale required by it is drawn so that the two-layer MLP is adequate for VQ encoding or recognition. Simulation experiments are presented to test the theoretical analysis
  • Keywords
    encoding; neural nets; exponential encoding complexity; multilayer perceptron; neural network; vector quantization; Analytical models; Computational modeling; Encoding; Helium; Multi-layer neural network; Multilayer perceptrons; Neck; Neural networks; Pattern recognition; Vector quantization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics, Control and Instrumentation, 1991. Proceedings. IECON '91., 1991 International Conference on
  • Conference_Location
    Kobe
  • Print_ISBN
    0-87942-688-8
  • Type

    conf

  • DOI
    10.1109/IECON.1991.239070
  • Filename
    239070