• DocumentCode
    2498092
  • Title

    Stability analysis of self-organizing maps and vector quantization algorithms

  • Author

    Tucci, Mauro ; Raugi, Marco

  • Author_Institution
    Dept. of Electr. Syst. & Autom., Univ. of Pisa, Pisa, Italy
  • fYear
    2010
  • fDate
    18-23 July 2010
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    In this work a zero input stability analysis of the self-organizing map (SOM) learning algorithm and related unsupervised vector quantization algorithms is presented. The stability of the SOM incremental learning rule is analyzed using the theory of dynamical switched systems. The equation is demonstrated to be asymptotically stable under simple constraints on the learning parameters.
  • Keywords
    asymptotic stability; learning (artificial intelligence); self-organising feature maps; vector quantisation; asymptotically stable; dynamical switched systems; learning algorithm; self-organizing maps; stability analysis; unsupervised vector quantization algorithms; Algorithm design and analysis; Asymptotic stability; Convergence; Heuristic algorithms; Kernel; Stability analysis; Vector quantization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2010 International Joint Conference on
  • Conference_Location
    Barcelona
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-6916-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2010.5596939
  • Filename
    5596939
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2498092