Summary

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In this chapter we learned about the Kohonen neural network. The Kohonen neural network differs from the feed forward back propagation network in several ways. The Kohonen neural network is trained in an unsupervised way. This means that the Kohonen neural network is given input data but no anticipated output. The Kohonen neural network then begins to map the training samples to each of its output neurons during training.

A Kohonen neural network contains only two levels. The network is presented with an input pattern that is given to the input layer. This input pattern must be normalized to numbers in the range between -1 and 1. The output from this neural network will be one single winning output neuron. The output neurons can be thought of as groups that the Kohonen neural network has classified the input as part of.

Training a Kohonen neural network is considerably different than the back propagation algorithm that we examined in Chapter 5. To train the Kohonen neural network we present it with the training elements and see which output neuron "wins". This winning neuron's weights are them modified so that it will activate higher on the pattern that caused it to win.

There is also a case where there may be one or more neurons that fail to ever win. Such neurons are dead-weight to the neural network. We must identify such neurons and cause them to recognize patterns that are already recognized by other more "overworked" neurons. This causes the burden of recognition to fall more evenly over the output neurons.

This chapter presented only a simple example of the Kohonen neural network. In the next chapter we will apply the Kohonen neural network to a more real-world application. We will see how to use the Kohonen neural network to recognize handwriting.