Abstract
This paper proposes an alternative adjustment of weights for Kohonen learning that provides a better match of topology and an improved representation of the input data. Based on the adjustment of weights, a new weighted energy function is defined. Then we propose a new scheme that applies the multiple training concept to the weighted energy function. The new scheme results in a better directional adjustment of weights and increases the training speed of the network dramatically. Finally experimental results demonstrate that much better recognition rate is obtained.
Original language | English |
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Pages (from-to) | 747-751 |
Number of pages | 5 |
Journal | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
Volume | 1 |
State | Published - 1996 |
Event | Proceedings of the 1996 IEEE International Conference on Systems, Man and Cybernetics - Beijing, China Duration: 14 Oct 1996 → 17 Oct 1996 |