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Approximating I/O Data Using Radial Basis Functions: A New Clustering-Based Approach

  • Mohammed Awad
  • Héctor Pomares
  • Luis Javier Herrera
  • Jesús González
  • Alberto Guillén
  • Fernando Rojas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3512)

Abstract

In this paper, we deal with the problem of function approximation from a given set of input/output data. This problem consists of analyzing these training examples so that we can predict the output of the model given new inputs. We present a new method for function approximation of the I/O data using radial basis functions (RBFs). This approach is based on a new efficient method of clustering of the centres of the RBF Network (RBFN); it uses the objective output of the RBFN to move the clusters instead of just the input values of the I/O data. This method of clustering, especially designed for function approximation problems, improves the performance of the approximator system obtained, compared with other models derived from traditional algorithms.

Keywords

Radial Basis Function Singular Value Decomposition Function Approximation Radial Basis Function Network Normalize Root Mean Square Error 
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 2005

Authors and Affiliations

  • Mohammed Awad
    • 1
  • Héctor Pomares
    • 1
  • Luis Javier Herrera
    • 1
  • Jesús González
    • 1
  • Alberto Guillén
    • 1
  • Fernando Rojas
    • 1
  1. 1.Dept. of Computer Architecture and Computer TechnologyUniversity of GranadaGranadaSpain

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