C++ Neural Networks and Fuzzy Logic: Preface

The Neighborhood Size and Alpha

In the Kohonen map, a parameter called the neighborhood size is used to model the effect of the mexican hat

function. Those neurons that are within the distance specified by the neighborhood size participate in training

and weight vector changes; those that are outside this distance do not participate in learning. The

neighborhood size typically is started as an initial value and is decreased as the input pattern cycles continue.

This process tends to support the winner−take−all strategy by eventually singling out a winner neuron for a

given pattern.

Figure 11.3 shows a linear arrangement of neurons with a neighborhood size of 2. The hashed central neuron

is the winner. The darkened adjacent neurons are those that will participate in training.

Figure 11.3

  Winner neuron with a neighborhood size of 2 for a Kohonen map.

Besides the neighborhood size, alpha typically is also reduced during simulation. You will see these features

when we develop a Kohonen map program.

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C++ Neural Networks and Fuzzy Logic:Preface

The Neighborhood Size and Alpha

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