A synergetic neural network with crosscorrelation dynamics

Abstract

In this study we shall put forward a bidirectional synergetic neural network and investigate the crossassociation dynamics in an order parameter space. The present model is substantially based on a top-down formulation of the dynamic rule of an analog neural network in the analogy with the conventional bidirectional associative memory. It is proved that a complete association can be assured up to the same number of the embedded patterns as the number of neurons. In addition, a searching process of a couple of embedded patterns can be also realised by means of controlling attraction parameters as seen in the autoassociative synergetic models.