caa a22 4500 475801105 CHVBK 20180406123737.0 cr unu---uuuuu 170329e20000601xx s 000 0 eng 10.1023/A:1009663626444 doi (NATIONALLICENCE)springer-10.1023/A:1009663626444 Fiori Simone Department of Electronics and Automatics, University of Ancona, Ancona, Italy aut An Experimental Comparison of Three PCA Neural Networks [Elektronische Daten] [Simone Fiori] 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. Kluwer Academic Publishers, 2000 principal component analysis nationallicence generalized Hebbian learning nationallicence adaptive principal-component extraction nationallicence Neural Processing Letters Kluwer Academic Publishers 11/3(2000-06-01), 209-218 1370-4621 11:3<209 2000 11 11063 https://doi.org/10.1023/A:1009663626444 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1023/A:1009663626444 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Fiori Simone Department of Electronics and Automatics, University of Ancona, Ancona, Italy aut NATIONALLICENCE 773 0- Neural Processing Letters Kluwer Academic Publishers 11/3(2000-06-01), 209-218 1370-4621 11:3<209 2000 11 11063 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer