Abstract
An adaptive neural network with asymmetric connections is introduced. This network is related to the Hopfield network with graded neurons and uses a recurrent generalization of the \ensuremath{\delta} rule of Rumelhart, Hinton, and Williams to modify adaptively the synaptic weights. The new network bears a resemblance to the master/slave network of Lapedes and Farber but it is architecturally simpler.
Keywords
GeneralizationHopfield networkArtificial neural networkComputer scienceRecurrent neural networkArtificial intelligenceBackpropagationTypes of artificial neural networksMathematics
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Publication Info
- Year
- 1987
- Type
- article
- Volume
- 59
- Issue
- 19
- Pages
- 2229-2232
- Citations
- 949
- Access
- Closed
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Cite This
Fernando J. Pineda
(1987).
Generalization of back-propagation to recurrent neural networks.
Physical Review Letters
, 59
(19)
, 2229-2232.
https://doi.org/10.1103/physrevlett.59.2229
Identifiers
- DOI
- 10.1103/physrevlett.59.2229