• DocumentCode
    301606
  • Title

    Supervised adaptive resonance theory neural network for modelling dynamic systems

  • Author

    Pham, D.T. ; Sukkar, M.F.

  • Author_Institution
    Sch. of Eng., Univ. of Wales Coll. of Cardiff, UK
  • Volume
    3
  • fYear
    1995
  • fDate
    22-25 Oct 1995
  • Firstpage
    2500
  • Abstract
    A supervised neural network, SMART2, has been developed which can be used with the ART2 algorithm for modelling discrete dynamic systems. A new layer has been added as a higher transformation stage to provide an output mapping field. The connection between the new field and the category field has been made by long term memory adaptive filters. Top-down adaptive filters in the new field have been employed to code the output prototype. Error equations have been derived to trace errors in the model and train the new network. The proposed network has been shown in simulation to be able to represent arbitrary dynamic systems. Results presented in this paper demonstrate the effectiveness of the network
  • Keywords
    ART neural nets; adaptive filters; content-addressable storage; discrete time systems; learning (artificial intelligence); modelling; SMART2; adaptive resonance theory; discrete dynamic systems; dynamic system modelling; long term memory; output mapping field; supervised neural network; top-down adaptive filters; Adaptive filters; Adaptive systems; Data structures; Equations; Feedback; Neural networks; Prototypes; Resonance; Subspace constraints; Supervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7803-2559-1
  • Type

    conf

  • DOI
    10.1109/ICSMC.1995.538157
  • Filename
    538157