Recurrent Cascade-Correlation (RCC) is a recurrent version of Cascade-Correlation and can be used to train recurrent neural nets ([Elm90]).
Recurrent nets have some features that distinguish them from normal neural networks. For example they can be used to represent time implicitly rather than explicitly. One of the most commonly known architectures of recurrent neural nets is the Elman model, which assumes that the network operates in discrete time steps. The outputs of the network's hidden units at a time t are fed back for use as additional network inputs at time t+1. To store the output of the hidden units Elman introduced context units, which represent a kind of short-term memory (see fig. ). To integrate the Elman model into the cascade architecture some changes are necessary: The hidden units' values are no longer fed back to all other hidden units. Instead every hidden unit has only one self recurrent link as shown in figure . This self recurrent link is trained along with the candidate unit's other input weights to maximize the correlation. When the candidate unit is added to the active network as hidden unit, the recurrent link is frozen along with all other links.
Figure: RCC architecture of a recurrent neural net.
Figure: The Elman architecture of a recurrent neural net