A recursive network is only a recurrent network generalization. In a recurrent network, weights are exchanged (and dimensionality stays constant) over the sequence and, in a test cycle, you can see a list of varying lengths then you will find in train times while working with position-dependent weights. For the same reason, the weights are distributed in a recursive network (and dimensionality stays constant).
In order to understand Recurrent Neural Networks (RNN), it is first necessary to understand the working principle of a feedforward network. In short, we can say that it is a structure that produces output by applying some mathematical operations to the information coming to the neurons on the layers.
The information received in the Feedforward working structure is only processed forward. In this structure, an output value is obtained by passing the input data through the network. The error is obtained by comparing the obtained output value with the correct values. The weight values on the network are changed depending on the error, and in this way, a model that can give the most accurate result is created.
Non-linear adaptive models that can learn in-depth and structured information are called Recursive Neural Networks (RvNNs). RvNNs were effective in natural language processing for learning sequences and structures of the trees, primarily phrases, and sentences based on word embedding.
RvNN is the connections between neurons are established in directed cycles. These models have however not yet been universally recognized. The key explanation for this is its underlying ambiguity. Not only for being highly complex structures for information retrieval but also because of a costly computational learning period.
RvNN is more of a hierarchy, where the input series actually is without time aspects, but the input must be hierarchically interpreted in a tree-type manner.
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