• DocumentCode
    2617139
  • Title

    A learning rule in the Chebyshev norm for multilayer perceptrons

  • Author

    Burrascano, P. ; Lucci, P.

  • Author_Institution
    INFO-COM Dept., Roma Univ., Italy
  • fYear
    1990
  • fDate
    1-3 May 1990
  • Firstpage
    211
  • Abstract
    An L∞ version of the back-propagation paradigm is proposed. A comparison between the L2 and the L∞ paradigms is presented, taking into account computational cost and speed of convergence. It is shown how the learning process can be formulated as an optimization problem. Experimental results from two test cases of the convergence of the L ∞ algorithm are presented
  • Keywords
    learning systems; neural nets; optimisation; Chebyshev norm; back-propagation paradigm; computational cost; convergence; learning process; learning rule; multilayer perceptrons; optimization problem; test cases; Approximation error; Chebyshev approximation; Feedforward neural networks; Intelligent networks; Multi-layer neural network; Multilayer perceptrons; Neural networks; Neurons; Probability density function; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 1990., IEEE International Symposium on
  • Conference_Location
    New Orleans, LA
  • Type

    conf

  • DOI
    10.1109/ISCAS.1990.111987
  • Filename
    111987
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2617139