A new model of neural associative memories

Abstract

A novel model of discrete neural associative memories is presented. The most important feature of this model is that static mapping instead of the dynamic convergent process is used to retrieve the stored messages. The model features a two-layer structure, with feedforward connections only and using two kinds of neurons. This model uses an extremely simple weight set-up rule and all the resulting weights can only be -1 or +1. Compared to the Hopfield model, the model can guarantee all the given patterns to be stored as fixed points. Each fixed point is surrounded by an attraction ball with the maximum possible radius. The processing speed is much higher because of the use of layered feedforward nets. The model is flexible in the sense that extra patterns can be easily incorporated into the established net.<>