Quickly trained artificial neural network with single hidden layer Gaussian units

Goutam Chakraborty, Norio Shiratori, Shoichi Noguchi

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Nonlinear radial basis functions at the single layer hidden units are effective in generating complex nonlinear mapping and at the same time facilitate fast linear learning. In this work, we propose a model and an algorithm to arrive at a near optimum initial configuration very quickly. Thus the position of the hidden units in the input space and the connection weights from the hidden units to the output units, instead of arbitrarily, are optimally set. Simulations on this initial configuration are performed. Different parameters are further trained and their effects experimented.

    Original languageEnglish
    Title of host publication1993 IEEE International Conference on Neural Networks
    Editors Anon
    Place of PublicationPiscataway, NJ, United States
    PublisherPubl by IEEE
    Pages466-472
    Number of pages7
    ISBN (Print)0780312007
    Publication statusPublished - 1993
    Event1993 IEEE International Conference on Neural Networks - San Francisco, CA, USA
    Duration: 1993 Mar 281993 Apr 1

    Other

    Other1993 IEEE International Conference on Neural Networks
    CitySan Francisco, CA, USA
    Period93/3/2893/4/1

      Fingerprint

    ASJC Scopus subject areas

    • Engineering(all)

    Cite this

    Chakraborty, G., Shiratori, N., & Noguchi, S. (1993). Quickly trained artificial neural network with single hidden layer Gaussian units. In Anon (Ed.), 1993 IEEE International Conference on Neural Networks (pp. 466-472). Publ by IEEE.