C++ Neural Networks and Fuzzy Logic: Preface

Bidirectional Associative Memory

Bidirectional Associative Memory, (BAM), and other models described in this section were developed by

Bart Kosko. BAM is a network with feedback connections from the output layer to the input layer. It

associates a member of the set of input patterns with a member of the set of output patterns that is the closest,

and thus it does heteroassociation. The patterns can be with binary or bipolar values. If all possible input

patterns are known, the matrix of connection weights can be determined as the sum of matrices obtained by

taking the matrix product of an input vector (as a column vector) with its transpose (written as a row vector).

The pattern obtained from the output layer in one cycle of operation is fed back at the input layer at the start of

the next cycle. The process continues until the network stabilizes on all the input patterns. The stable state so

achieved is described as resonance, a concept used in the Adaptive Resonance Theory.

You will find in Figure 8.1 in Chapter 8, the layout of the nodes that represent the neurons in a BAM network

and the connections between them. There are two fields of neurons. The network is fully connected with

feedback connections and forward connections. There are no lateral or recurrent connections.

Fuzzy Associative memories are similar to Bidirectional Associative memories, except that association is

established between fuzzy patterns. Chapter 9 deals with Fuzzy Associative memories.

C++ Neural Networks and Fuzzy Logic:Preface

Backpropagation

97

---