• DocumentCode
    295818
  • Title

    Growing filters for finite impulse response networks

  • Author

    Diepenhorst, M. ; Nijhuis, J.A.G. ; Venema, R.S. ; Spaanenburg, L.

  • Author_Institution
    Dept. of Comput. Sci., Groningen Univ., Netherlands
  • Volume
    2
  • fYear
    1995
  • fDate
    Nov/Dec 1995
  • Firstpage
    854
  • Abstract
    Time-delay neural networks are well-suited for prediction purposes. A particular implementation is the finite impulse response (FIR) neural net. A major design problem exists in establishing the optimal order of such filters while minimizing the number of weights. Here, a constructive solution inspired by cascade learning is outlined and illustrated by some typical case-studies
  • Keywords
    FIR filters; forecasting theory; learning (artificial intelligence); neural nets; time series; cascade learning; finite impulse response networks; prediction; time-delay neural networ; Auditory system; Biological processes; Biological system modeling; Delay; Feedforward systems; Finite impulse response filter; Information filtering; Information filters; Neural networks; Neurons;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1995. Proceedings., IEEE International Conference on
  • Conference_Location
    Perth, WA
  • Print_ISBN
    0-7803-2768-3
  • Type

    conf

  • DOI
    10.1109/ICNN.1995.487530
  • Filename
    487530
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=295818