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Functional Networks and the Lagrange Polynomial Interpolation

  • Cristina Solares
  • Eduardo W. Vieira
  • Roberto Mínguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

A new approach is presented for the approximation of a scalar function defined on a discrete set of points. The method is based on the application of functional networks and the Lagrange interpolation formula. The interpolation mechanism of the separable functional networks when the neuron functions are approximated by Lagrange polynomials, is explored. The coefficients of the Lagrange interpolation formula are estimated during the learning of the functional network by simply solving a linear system of equations. Finally, several examples show the effectiveness of the proposed interpolation method.

Keywords

Root Mean Square Error Nodal Point Neuron Function Functional Network Training Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Cristina Solares
    • 1
  • Eduardo W. Vieira
    • 1
  • Roberto Mínguez
    • 1
  1. 1.University of Castilla-La ManchaSpain

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