• DocumentCode
    1512490
  • Title

    On blind separation of complex-valued sources by extended Hebbian learning

  • Author

    Fiori, Simone

  • Author_Institution
    Dept. of Ind. Eng., Perugia Univ., Italy
  • Volume
    8
  • Issue
    8
  • fYear
    2001
  • Firstpage
    217
  • Lastpage
    220
  • Abstract
    The aim of this letter is to present a nonlinear extension to Sanger´s (1989) generalized Hebbian learning algorithm for complex-valued data neural processing, which allows for separating mixed independent circular source signals. The proposed generalization relies on an interesting interpretation of nonclassical Hebbian learning proposed by Sudjianto and Hassoun (1994) for real-valued neural units.
  • Keywords
    Hebbian learning; neural nets; signal processing; blind separation; complex-valued data neural processing; complex-valued sources; extended Hebbian learning; generalization; mixed independent circular source signals; nonlinear extension; real-valued neural units; Computer simulation; Data mining; Hebbian theory; Neural networks; Neurons; Principal component analysis; Signal analysis; Signal processing; Signal processing algorithms; Source separation;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1070-9908
  • Type

    jour

  • DOI
    10.1109/97.935735
  • Filename
    935735
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1512490