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A Learning Algorithm for Self-Organizing Maps Based on a Low-Pass Filter Scheme
In this work a novel training algorithm is proposed for the formation of topology preserving maps. In the proposed algorithm the weights are updated incrementally by using a higher-order difference equation, which implements a low pass digital filter. It is shown that by suitably choosing the filter the learning process can adaptively follow a specific dynamic.
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