Abstract

implementation Self-organization can be efficiently implemented based on Euclidian distance and global supervision. It is not necessary to explicitly model the connections between the units in the network. Every unit computes the distance between its weight vector and the input vector. An external supervisor finds the unit with the smallest distance, looks up the current neighborhood radius from a training schedule, and tells the units within this radius to modify their input weights. The weight adaptations are proportional to the Euclidian difference. The weights of unit (i; j) in a 2-D map are (a) 0 samples (b) 30 samples (c) 100 samples (d) 10,000 samples Figure 1: Abstract implementation of self-organization. The map consists of 20 \Theta 20 units in a 2-D array organization. The weight vector of each unit is shown as a point on the unit square 0 x; y 1. Each vector is connected with a line to the weight vectors of the four neighboring units. In other words, each intersection ...

Citations

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