C++ Neural Networks and Fuzzy Logic: Preface

2

. Now Y

2

 is passed back to the input layer through the feedback connections to

get the activation of the input layer as (–2, 2, 2, –2), which after thresholding gives the output vector (0, 1, 1,

0), which is X

2

.

The two vector pairs chosen here for encoding worked out fine, and the BAM network with

four neurons in Field A and three neurons in Field B is all set for finding a vector under

heteroassociation with a given input vector.

Continuation of Example

Let us now use the vector X

3

 = (1, 1, 1, 1). The vector Y

3

 = (0, 1, 1) is obtained at the output layer. But the

next step in which we present Y

3

 in the backward direction does not produce X

3

, instead it gives an X