• DocumentCode
    765533
  • Title

    A novel approach to the convergence of neural networks for signal processing

  • Author

    Liu, Ruey-wen ; Huang, Yih-fang ; Ling, Xie-Ting

  • Author_Institution
    Dept. of Electr. Eng., Notre Dame Univ., IN, USA
  • Volume
    42
  • Issue
    3
  • fYear
    1995
  • fDate
    3/1/1995 12:00:00 AM
  • Firstpage
    187
  • Lastpage
    190
  • Abstract
    A novel deterministic approach to the convergence analysis of (stochastic) learning algorithms is presented. The link between the two is the new concept of time-average invariance, which is a property of deterministic signals but resembles that of stochastic signals which are ergodic and stationary
  • Keywords
    neural nets; signal processing; stochastic processes; unsupervised learning; convergence analysis; deterministic approach; neural networks; signal processing; stochastic learning algorithms; stochastic signals; time-average invariance; Active filters; Algorithm design and analysis; Circuit analysis; Convergence; Equations; Neural networks; Signal analysis; Signal processing; Signal processing algorithms; Stochastic processes;
  • fLanguage
    English
  • Journal_Title
    Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1057-7122
  • Type

    jour

  • DOI
    10.1109/81.376866
  • Filename
    376866
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=765533