C++ Neural Networks and Fuzzy Logic: Preface

Competitive Learning

A Kohonen feature map may be used by itself or as a layer of another neural network. A Kohonen layer is

composed of neurons that compete with each other. Like in Adaptive Resonance Theory, the Kohonen SOM

is another case of using a winner−take−all strategy. Inputs are fed into each of the neurons in the Kohonen

layer (from the input layer). Each neuron determines its output according to a weighted sum formula:

Output = £ w

ij

 x

i

The weights and the inputs are usually normalized, which means that the magnitude of the weight and input

vectors are set equal to one. The neuron with the largest output is the winner. This neuron has a final output of

1. All other neurons in the layer have an output of zero. Differing input patterns end up firing different winner

neurons. Similar or identical input patterns classify to the same output neuron. You get like inputs clustered

together. In Chapter 12, you will see the use of a Kohonen network in pattern classification.