C++ Neural Networks and Fuzzy Logic: Preface

1

 = (1, 0,

1, 1). We already have X

1

 associated with Y

1

. This means that X

3

 is not associated with any vector in the

output space. On the other hand, if instead of getting X

1

 we obtained a different X

4

 vector, and if this in the

feed forward operation produced a different Y vector, then we repeat the operation of the network until no

changes occur at either end. Then we will have possibly a new pair of vectors under the heteroassociation

established by this BAM network.

Special Case—Complements

If a pair of (distinct) patterns X and Y are found to be heteroassociated by BAM, and if you input the

complement of X, complement being obtained by interchanging the 0’s and 1’s in X, BAM will show that the

complement of Y is the pattern associated with the complement of X. An example will be seen in the

illustrative run of the program for C++ implementation of BAM, which follows.