• DocumentCode
    353260
  • Title

    Stiefel-Grassman flow (SGF) learning: further results

  • Author

    Fiori, Simone

  • Author_Institution
    Dept. of Ind. Eng., Perugia Univ., Italy
  • Volume
    3
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    343
  • Abstract
    The aim of this this paper is to present recent contributions to Stiefel-Grassman flow (SGF) learning algorithms, a new class of learning paradigms for neural layers which allow for orthonormal signal/data processing. SGF learning has been introduced by the present author in 1996 as a way of training linear neural layers dedicated to blind source separation. In the meantime, several contributions have appeared in the scientific literature concerning the same topic, thus the study of a general framework explaining the different results has become necessary. In previous papers we presented a learning theory which appeared general enough to encompass the existing approaches; in this paper the latest results found are reported and discussed and references are given to computer simulations performed in order to test the effectiveness of the algorithms
  • Keywords
    learning (artificial intelligence); multilayer perceptrons; signal processing; SGF learning; Stiefel-Grassman flow learning; blind source separation; linear neural layers; Blind source separation; Equations; Industrial engineering; Jacobian matrices; Neurons; Signal processing; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
  • Conference_Location
    Como
  • ISSN
    1098-7576
  • Print_ISBN
    0-7695-0619-4
  • Type

    conf

  • DOI
    10.1109/IJCNN.2000.861328
  • Filename
    861328