Abstract
In contrast to conventional feedback bidirectional associative Memory (BAM) network models, a feedforward BAM network is developed based on a one-shot design algorithm of O(p(2)(n+m)) computational complexity, where p is the number of prototype pairs and n, m are the dimensions of the input/output bipolar vectors. The feedforward BAM is an n-p-m three-layer network of McCulloch-Pitts neurons with storage capacity 2(min{m,n}) and guaranteed perfect bidirectional recall. The overall network design procedure is fully scalable in the sense that any number p= or <2(min{m,n}) of bidirectional associations can be implemented. The prototype patterns may be arbitrarily correlated. With respect to inference performance, it is shown that the Hamming attractive radius of each prototype reaches the maximum possible value. Simulation studies and comparisons illustrate and support these theoretical developments.
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