A learning algorithm to obtain self-organizing maps using fixed neighbourhood Kohonen networks
In this paper, a learning algorithm that leads to an efficient self-organization in a Kohonen Neural Network (KNN) with fixed neighbourhood is presented. This algorithm may be faster than the originally proposed for KNNs, produces in general better covering of the input stimulus space, and can be more easily implemented in hardware due to the fixed neighbourhood it manages.
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