Neural Processing Letters

, Volume 11, Issue 3, pp 209–218

An Experimental Comparison of Three PCA Neural Networks

  • Simone Fiori

DOI: 10.1023/A:1009663626444

Cite this article as:
Fiori, S. Neural Processing Letters (2000) 11: 209. doi:10.1023/A:1009663626444


We present a numerical and structural comparison of three neural PCA techniques: The GHA by Sanger, the APEX by Kung and Diamantaras, and the ψ–APEX first proposed by the present author. Through computer simulations we illustrate the performances of the algorithms in terms of convergence speed and minimal attainable error; then an evaluation of the computational efforts for the different algorithms is presented and discussed. A close examination of the obtained results shows that the members of the new class improve the numerical performances of the considered existing algorithms, and are also easier to implement.

principal component analysisgeneralized Hebbian learningadaptive principal-component extraction

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Simone Fiori
    • 1
    • 2
  1. 1.Department of Electronics and AutomaticsUniversity of AnconaAnconaItaly
  2. 2.Department of Industrial EngineeringUniversity of PerugiaPerugiaItaly