An Experimental Comparison of Three PCA Neural Networks

摘要

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.