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An Improvement of Performance for Cascade Correlation Learning Algorithm using a Cosine Modulated Gaussian Activation Function

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2006, 11(3), pp.107-116
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

LEE SANG HWA 1 HAESANG SONG 1

1서원대학교

Candidate

ABSTRACT

This paper presents a new class of activation functions for Cascade Correlation learning algorithm, which herein will be called CosGauss function. This function is a cosine modulated gaussian function. In contrast to the sigmoidal, hyperbolic tangent and gaussian functions, more ridges can be obtained by the CosGauss function. Because of the ridges, it is quickly convergent and improves a pattern recognition speed. Consequently it will be able to improve a learning capability. This function was tested with a Cascade Correlation Network on the two spirals problem and results are compared with those obtained with other activation functions.

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