An Experimental Comparison of Three PCA Neural Networks
- Simone Fiori
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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.
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- An Experimental Comparison of Three PCA Neural Networks
Neural Processing Letters
Volume 11, Issue 3 , pp 209-218
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers
- Additional Links
- principal component analysis
- generalized Hebbian learning
- adaptive principal-component extraction
- Industry Sectors
- Simone Fiori (1) (2)
- Author Affiliations
- 1. Department of Electronics and Automatics, University of Ancona, Ancona, Italy
- 2. Department of Industrial Engineering, University of Perugia, Perugia, Italy