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Evolutionary Algorithm vs. Other Methods for Constructive Optimisation of RBF Network Kernels

  • Jan Koszlaga
  • Paweł Strumiłło
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 19)

Abstract

Three methods for optimising Radial Basis Function (RBF) neural network receptive field are compared in the paper, namely: gradient descent, simulation annealing, and evolutionary algorithm. An incremental RBF network training scheme is considered, i.e., in which RBF kernels are added one at the time and individually optimised. Algorithmic implementations of the tested optimisation methods for configuring the RBF receptive field are shown and their computation costs are compared. The considered optimisation methods yield excellent results for the classification benchmark of Iris flowers. For the genetic optimisation scheme three RBF Gaussian kernels are sufficient to achieve average classification accuracy of the Irises at the level of 98%.

Keywords

Radial Basis Function Radial Basis Function Neural Network Radial Basis Function Kernel Network Error Average Classification Accuracy 
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 2003

Authors and Affiliations

  • Jan Koszlaga
    • 1
  • Paweł Strumiłło
    • 1
  1. 1.Institute of ElectronicsTechnical University of ŁódźŁódźPoland

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