Abstract
We show how partial reduction of self-connections of the network designed with the pseudo-inverse learning rule increases the direct attraction radius of the network. Theoretical formula is obtained. Data obtained by simulation are presented.
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References
L. Personnaz, I. Guyon and G. Dreyfus,“Collective computational properties of neural networks: new learning mechanisms”, Physical Review A, Vol. 34, pp. 4217–4228, 1986.
D.O. Gorodnichy, “A wayto improve error correction capability of Hopfield associative memory in the case of saturation”, HELNET 94–95 International Workshop on Neural Networks Proceedings, Vol. I/II, pp. 198–212, VU University Press, Amsterdam, 1996.
T. Kohonen, Self-Organization and Associative Memory, Springer-Verlag: Berlin, 1984.
J. Hertz, A. Krogh and R.G. Palmer, Introductionto the Theory of Neural Computation, Addison-Wesley: Redwood City, CA, 1991.
I. Kanter and H. Sampolinsky,“Associative recall of memory without errors”, Physical Review A, Vol. 35, pp. 380–392, 1987.
P. Floreen and P. Orponen, “Attraction radii in binary Hopfield nets are hard to compute”, Neural Computation, Vol. 5, pp. 812–821, 1993.
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Gorodnichy, D.O., Reznik, A.M. Increasing Attraction of Pseudo-Inverse Autoassociative Networks. Neural Processing Letters 5, 51–55 (1997). https://doi.org/10.1023/A:1009614025059
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DOI: https://doi.org/10.1023/A:1009614025059