An experimental comparison of three PCA neural networks

We present a numerical and structural comparison of three neural PCA techniques: The GHA by Sanger, the APEX by Kung and Diamantaras, and the psi-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.