Complexity preserving increase of the capacity of bidirectional associative memories by dilation and translation

In this paper, we show how to increase the capacity of Kosko-type bidirectional associative memories by introducing dilation and translation parameters in the pattern space. The essential point of this approach is that the increase of the capacity of the networks almost doesn't affect their complexity. The general idea of the proposed strategy is to tune the networks in such a way that they are better suited to store patterns which are highly correlated without touching their ability to recover easy-to-store information. A detailed example at the end of the paper shows how our approach works in practice.