• DocumentCode
    1748946
  • Title

    Adaptive resonance theory networks using incremental communication

  • Author

    Chen, Ming ; Ghorbani, Ali A. ; Bhavsar, Virendra C.

  • Author_Institution
    Fac. of Comput. Sci., New Brunswick Univ., Fredericton, NB, Canada
  • Volume
    4
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    2774
  • Abstract
    The incremental inter-node communication method is applied to the adaptive resonance theory 2 (ART2) networks. The incremental communication is aimed at reducing the communication costs of parallel and VLSI implementations of artificial neural networks. An ART2 node architecture incorporating the incremental communication is presented. A simulator is developed to study the behavior of ART2 networks with varying precisions of incremental data communication. Experiments are carried out to study the effects of the incremental communication on the convergence and savings in communication costs. We have found that even 7-bit precision in fixed-point and 13-bit (including 8-bit exponent) floating-point representations may be sufficient for the network to give the same results as those with conventional communication using 32-bit precision. The simulation results show that the limited precision errors are bounded and do not seriously affect the convergence of ART2 networks
  • Keywords
    ART neural nets; communication complexity; convergence; 13 bit; 32 bit; 7 bit; 8 bit; ART neural networks; ART2 networks; ART2 node architecture; VLSI implementations; adaptive resonance theory 2 networks; communication costs; convergence; incremental inter-node communication; parallel implementations; Adaptive systems; Artificial neural networks; Computational modeling; Computer science; Convergence; Costs; Data communication; Niobium; Resonance; Very large scale integration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7044-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2001.938812
  • Filename
    938812