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14a, b and c demonstrates a simple solid pattern for the first 150 iterations of TurSOM. The CLR determines which connections will be eliminated. The connections that are not considered optimal are removed and as evident by the figure, the removed connections were negatively impacting the network. So far, we have described how TurSOM may separate into different networks, but we have not addressed how two networks can rejoin into one. 2), becomes active in TurSOM when two neurons disconnect from one another.

However, each neuron is composed of or “filled with” a feed-forward network, such as a multi-layer perceptron (MLP). The major difference between SOMs and feed-forward networks, is that SOMs learn the topology or structure of data. Feed-forward architectures learn functions about input. The effective outcome of this network is that it self-organizes function space. That is to say, when presented with various types of input patterns where functional knowledge might be very important, mnSOM is able to topologically order functions based on similarity.

To form the lattice structure correctly, the connections between known neighbors should be illustrated. The neurons are often recorded in one- or two-dimensional arrays, to allow the physical neighbors of each neuron to be recorded. 24 Machine Learning 3. 1 Fritzke’s growing SOM variants Self-organizing Maps, as introduced by Kohonen, are static sized network. The obvious disadvantage to the predetermined number of neurons is that number is either not high enough to adequately map the input space or too high, thus leaving many neurons underutilized.

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