Polynomial Higher Order Neural network for shift - invariant Pattern Recognition


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 4, No. 12, pp. 3063-3068, Dec. 1997
10.3745/KIPSTE.1997.4.12.3063,   PDF Download:

Abstract

In this paper, we have extended the generalization back-propagation algorithm to multi-layer polynomial higher order neural networks. The purpose of this paper is to describe various pattern recognition using polynomial higher-order neural network. And we have applied shift position T-C test pattern for invariant pattern recognition and measured generalization by mirror symmetry problem. Simulation result show that the ability for invariant pattern recognition increase with the proposed technique. Recognition rate of invariant T-C pattern is 90% effective and of mirror symmetry problem is 70% effective when the proposed technique is utilized. These results are much better than those by the convetional methods.


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Cite this article
[IEEE Style]
C. J. Su and H. S. Chan, "Polynomial Higher Order Neural network for shift - invariant Pattern Recognition," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 4, no. 12, pp. 3063-3068, 1997. DOI: 10.3745/KIPSTE.1997.4.12.3063.

[ACM Style]
Chung Jong Su and Hong Sung Chan. 1997. Polynomial Higher Order Neural network for shift - invariant Pattern Recognition. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 4, 12, (1997), 3063-3068. DOI: 10.3745/KIPSTE.1997.4.12.3063.