C++ Neural Networks and Fuzzy Logic: Preface

Program Details and Flow

A neuron in the first layer is referred to as anrn, and the number of neurons in this layer is referred to as

anmbr. We give the namebnrn to the array of neurons in the second layer, and bnmbr denotes the size of

that array. The sequence of operations in the program is as follows:

•  We ask the user to input the exemplar vectors, and we transform them into their bipolar versions.

The trnsfrm ( ) function in the exemplar class is for this purpose.

•  We give the network the X vector, in its bipolar version, in one exemplar pair. We find the

activations of the elements of bnrn array and get corresponding output vector as a binary pattern. If

this is the Y in the exemplar pair, the network has made a desired association in one direction, and we

go on to the next.step. Otherwise we have a potential associated pair, one of which is X and the other

is what we just got as the output vector in the opposite layer. We say potential associated pair because

we have the next step to confirm the association.