Synonyms
Definition
The simple recurrent network is a specific version of the Backpropagation neural network that makes it possible to process of sequential input and output (Elman, 1990). It is typically a three-layer network where a copy of the hidden layer activations is saved and used (in addition to the actual input) as input to the hidden layer in the next time step. The previous hidden layer is fully connected to the hidden layer. Because the network has no recurrent connections per se (only a copy of the activation values), the entire network (including the weights from the previous hidden layer to the hidden layer) can be trained with the backpropagation algorithm as usual. It can be trained to read a sequence of inputs into a target output pattern, to generate a sequence of outputs from a given input pattern, or to map an input sequence to an output sequence (as in predicting the next input). Simple recurrent networks have been...
Recommended Reading
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179–211.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this entry
Cite this entry
Miikkulainen, R. (2011). Simple Recurrent Network. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_762
Download citation
DOI: https://doi.org/10.1007/978-0-387-30164-8_762
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30768-8
Online ISBN: 978-0-387-30164-8
eBook Packages: Computer ScienceReference Module Computer Science and Engineering