A mapping neural network using unsupervised and supervised training

A two-layer mapping neural network called an extended differentiator network (EDN) is described. The network uses both unsupervised and supervised training in two phases. The differentiator, which is an unsupervised pattern classifier, is followed by the supervised outstar structure of Grossberg. This makes a network somewhat similar to the counterpropagation network of R. Hecht-Nielsen (1987). The unsupervised training of the input patterns by the differentiator provides useful information for the subsequent layer of the network and thus the associations with the target vectors are learned rapidly. As a result, some complex mappings are realizable by the network. The operation of the EDN is demonstrated by some simulation examples.<>