• DocumentCode
    288683
  • Title

    The best approximation to C2 functions and its error bounds using regular-center Gaussian networks

  • Author

    Liu, Binfan ; Si, Jennie

  • Author_Institution
    Dept. of Electr. Eng., Arizona State Univ., Tempe, AZ, USA
  • Volume
    4
  • fYear
    1994
  • fDate
    27 Jun-2 Jul 1994
  • Firstpage
    2400
  • Abstract
    Gaussian neural networks are considered to approximate any C2 function with support on the unit hypercube Im=[0,1] m in the sense of best approximation. An upper bound (0(N-2)) of the approximation error is obtained in the present paper for a Gaussian network having Nm hidden neurons with centers defined on a regular mesh in Im
  • Keywords
    approximation theory; feedforward neural nets; function approximation; optimisation; C2 functions; Gaussian neural networks; approximation; error bounds; hidden neurons; hypercube; radial basis network; upper bound; Approximation error; Artificial neural networks; Fourier transforms; Hypercubes; Interpolation; Neural networks; Neurons; Nonhomogeneous media; Polynomials; Upper bound;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
  • Conference_Location
    Orlando, FL
  • Print_ISBN
    0-7803-1901-X
  • Type

    conf

  • DOI
    10.1109/ICNN.1994.374595
  • Filename
    374595
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=288683