C++ Neural Networks and Fuzzy Logic: Preface

Normalization of a Vector

Consider a vector, A = ax + by + cz. The normalized vector A’ is obtained by dividing each component of A

by the square root of the sum of squares of all the components. In other words each component is multiplied

by 1/ [radic](a

2

 + b

2

 + c

2

). Both the weight vector and the input vector are normalized during the operation of

the Kohonen feature map. The reason for this is the training law uses subtraction of the weight vector from the

input vector. Using normalization of the values in the subtraction reduces both vectors to a unit−less status,

and hence, makes the subtraction of like quantities possible. You will learn more about the training law

shortly.

C++ Neural Networks and Fuzzy Logic:Preface

Chapter 11 The Kohonen Self−Organizing Map

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