C++ Neural Networks and Fuzzy Logic: Preface

1

 and Y

1

 are one exemplar pair, the third component of Y

1

 is what we need as the third component of

the output in the current cycle of operation; therefore, we fed X

1

 and received Y

1

. If we feed Y

1

 at the other

end (B field) , the activations in the A field will be (2, –2, –2, 2), and the output vector will be (1, 0, 0, 1),

which is X

1

.

With A field input X

2

 you get B field activations (4, −4, 0), giving the output vector as (1, 0, 1), which is Y

2

.

Thus X

2

 and Y

2

 are heteroassociated with each other.

Let us modify our X

1

 to be (1, 0, 1, 1). Then the weight matrix W becomes

        1  [−1  1  1]       −1  [1  −1  1]       −2    2    0

 W =   −1              + 1               =    2   −2    0

        1                    1                    0    0    2

        1                   −1                   −2    2    0

and

                  −2     2     0    −2

W

T

 =    2    −2     0     2

                   0     0     2     0

Now this is a different set of two exemplar vector pairs. The pairs are X