C++ Neural Networks and Fuzzy Logic: Preface

3

 = ( 1, 0, 0, 0), with which the activations of neurons in Field B

would be (−2, 2, 0). The first component of the output vector is clearly 0, and the second clearly 1. The third

component is what is in doubt. Considering the last row of the table where Y

2

 gives the state of the neurons in

Field B, you can accept 1, the last component of Y

2

, as the value you get from the thresholding function

corresponding to the activation value 0. So the output would be the vector (0, 1, 1), which is Y

1

. But Y

1

 is

heteroassociated with X

1

. Well, it means that X

3

 = ( 1, 0, 0, 0) is not heteroassociated with any X vector.

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C++ Neural Networks and Fuzzy Logic:Preface

Weights and Training

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